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  • 1.
    Abbaspour, Sara
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Surface EMG signal processing: Removing ECG interferences and classifying hand movements2017Ingår i: Medicinteknikdagarna 2017 MTD 2017, Västerås, Sweden, 2017Konferensbidrag (Refereegranskat)
  • 2.
    Abbaspour, Sara
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Electromyography signal analysis: Electrocardiogram artifact removal and classifying hand movements2018Ingår i: World Congress on Medical Physics and Biomedical Engineering IUPESM, 2018Konferensbidrag (Refereegranskat)
  • 3.
    Abdelakram, Hafid
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Abdullah, Saad
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Folke, Mia
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    Accurate bladder monitoring is critical in the management of conditions such as urinary incontinence, voiding dysfunction, and spinal cord injuries. Electrical bioimpedance (EBI) has emerged as a cost-effective and non-invasive approach to monitoring bladder activity in daily life, with particular relevance to patient groups who require measurement of bladder urine volume (BUV) to prevent urinary leakage. However, the impact of activities in daily living (ADLs) on EBI measurements remains incompletely characterized. In this study, we investigated the impact of normal ADLs such as sitting, standing, and walking on EBI measurements using the MAX30009evkit system with four electrodes placed on the lower abdominal area. We developed an algorithm to identify artifacts caused by the different activities from the EBI signals. Our findings demonstrate that various physical activities clearly affected the EBI measurements, indicating the necessity of considering them during bladder monitoring with EBI technology performed during physical activity (or normal ADLs). We also observed that several specific activities could be distinguished based on their impedance values and waveform shapes. Thus, our results provide a better understanding of the impact of physical activity on EBI measurements and highlight the importance of considering such physical activities during EBI measurements in order to enhance the reliability and effectiveness of EBI technology for bladder monitoring.

  • 4.
    Abdelakram, Hafid
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Difallah, Sabrina
    Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, 16111 Algiers, Algeria.
    Alves, Camille
    Assistive Technology Lab (NTA), Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38408-100, Brazil.
    Abdullah, Saad
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Folke, Mia
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review2023Ingår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 5, artikel-id 2758Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

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  • 5.
    Abdelakram, Hafid
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Abdullah, Saad
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Edu-Mphy: A Low-Cost Multi-Physiological Recording System for Education and Research in Healthcare and Engineering2023Ingår i: Abstracts: Medicinteknikdagarna 2023, 2023, s. 117-117Konferensbidrag (Övrigt vetenskapligt)
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  • 6.
    Abdullah, Saad
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system. Department of Biomedical Engineering, Riphah International University, Lahore, Pakistan.
    Abdelakram, Hafid
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Bilal Saeed, Muhammad
    Biomedical Engineering Department, NED University of Engineering and Technology, Karachi, Pakistan..
    Saad, Samreen
    Department of Biochemistry, Karachi University, Karachi, Pakistan.
    Real-Time Portable Raspberry Pi-Based System for Sickle Cell Anemia Detection2023Ingår i: Abstracts: Medicinteknikdagarna 2023, 2023, s. 118-118Konferensbidrag (Övrigt vetenskapligt)
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  • 7.
    Abdullah, Saad
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Abdelakram, Hafid
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Folke, Mia
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters2023Ingår i: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc. , 2023, s. 923-924Konferensbidrag (Refereegranskat)
    Abstract [en]

    Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

  • 8.
    Abdullah, Saad
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Hafid, Abdelakram
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Folke, Mia
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)2023Ingår i: Electronics, E-ISSN 2079-9292, Vol. 12, nr 5, artikel-id 1174Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

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  • 9.
    Abdullah, Saad
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Hafid, Abdelakram
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Folke, Mia
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points2023Ingår i: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, artikel-id 1199604Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

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  • 10.
    Abdullah, Saad
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Hafid, Abdelakram
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Shahid, H.
    Coventry University, Research Centre for Intelligent Healthcare, Coventry, United Kingdom.
    Comparing the Effectiveness of EMG and Electrical Impedance myography Measurements for Controlling Prosthetics2023Ingår i: IEEE Int. Multidiscip. Conf. Eng. Technol., IMCET, Institute of Electrical and Electronics Engineers Inc. , 2023, s. 189-193Konferensbidrag (Refereegranskat)
    Abstract [en]

    In recent years, the field of prosthetics has made significant progress towards creating prosthetic devices that are more functional, comfortable, and user-friendly. However, achieving intuitive control over prosthetic hand movements remains a significant challenge, especially for individuals with limb loss who rely on prosthetics for independent daily activities. To address this challenge, researchers have explored the potential of non-invasive techniques as electromyography (EMG) for prosthetic control. This paper aims to investigate the potential of using EMG and the electrical impedance myography (EIMG) techniques jointly for the measurement of hand movements. The study involved recording and comparing EMG and EIMG signals from a cohort of healthy individuals. These signals were captured during four distinct hand gestures: opening and closing the hand, as well as extending and flexing it, under varying time conditions, allowing for categorization into low and high-intensity movements. Data collection employed the Open BCI and ZRPI devices. The analysis of these signal waveforms revealed compelling results. Brachioradialis activity in EMG 2 exhibited an increase during open hand (0.015mV) and extension hand (0.009mV in low and 0.013mV in high intensity) gestures, accompanied by increased EIMG activity (56mV and 52mV respectively). Additionally, close hand (0.0018mV in low and 0.05mV in high intensity) and flexion hand (0.0075 in low intensity and 0.002 in high intensity) gestures exhibited heightened flexor carpi ulnaris activity with raised EIMG activity (57mV and 45mV respectively). These results proved to be consistent, acceptable, and aligned with existing literature. The findings of this paper indicate that both EMG and EIMG techniques could be used together to control custom-made hand prosthetics, demonstrating a significant development that could lead to more intuitive and easier-to-control prosthetics. Also, the results obtained could be valuable to researchers and engineers working in the prosthetics field, as it provides insights into the potential of non-invasive techniques for prosthetic control.

  • 11.
    Abdullah, Saad
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features2023Ingår i: Frontiers in Cardiovascular Medicine, E-ISSN 2297-055X, Vol. 10, artikel-id 1285066Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

  • 12.
    Afifi, S.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    GholamHosseini, Hamid
    Auckland University of Technology, Auckland, New Zealand.
    Sinha, R.
    Auckland University of Technology, Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Novel Medical Device for Early Detection of Melanoma2019Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 261, s. 122-127Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.

  • 13.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Personalized Health-Monitoring System for Elderly by Combining Rules and Case-based Reasoning2015Ingår i: Studies in Health Technology and Informatics, Volume 21: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, 2015, s. 249-254Konferensbidrag (Refereegranskat)
    Abstract [en]

    Health-monitoring system for elderly in home environment is a promising solution to provide efficient medical services that increasingly interest by the researchers within this area. It is often more challenging when the system is self-served and functioning as personalized provision. This paper proposed a personalized self-served health-monitoring system for elderly in home environment by combining general rules with a case-based reasoning approach. Here, the system generates feedback, recommendation and alarm in a personalized manner based on elderly’s medical information and health parameters such as blood pressure, blood glucose, weight, activity, pulse, etc. A set of general rules has used to classify individual health parameters. The case-based reasoning approach is used to combine all different health parameters, which generates an overall classification of health condition. According to the evaluation result considering 323 cases and k=2 i.e., top 2 most similar retrieved cases, the sensitivity, specificity and overall accuracy are achieved as 90%, 97% and 96% respectively. The preliminary result of the system is acceptable since the feedback; recommendation and alarm messages are personalized and differ from the general messages. Thus, this approach could be possibly adapted for other situations in personalized elderly monitoring.

  • 14.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Big Data Analytics in Health Monitoring at Home2017Ingår i: Medicinteknikdagarna 2017 MTD 2017, 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposed a big data analytics approach applied in the projects ESS-H and E-care@home in the context of biomedical and health informatics with the advancement of information fusion, data abstraction, data mining, knowledge discovery, learning, and reasoning [1][2]. Data are collected through the projects, considering both the health parameters, e.g. temperature, bio-impedance, skin conductance, heart sound, blood pressure, pulse, respiration, weight, BMI, BFP, movement, activity, oxygen saturation, blood glucose, heart rate, medication compliance, ECG, EMG, and EEG, and the environmental parameters e.g. force/pressure, infrared (IR), light/luminosity, photoelectric, room-temperature, room-humidity, electrical usage, water usage, RFID localization and accelerometers. They are collected as semi-structured/unstructured, continuous/periodic, digital/paper record, single/multiple patients, once/several-times, etc. and stored in a central could server [5]. Thus, with the help of embedded system, digital technologies, wireless communication, Internet of Things (IoT) and smart sensors, massive quantities of data (so called ‘Big Data’) with value, volume, velocity, variety, veracity and variability are achieved [2]. The data analysis work in the following three steps. In Step1, pre-processing, future extraction and selection are performed based on a combination of statistical, machine learning and signal processing techniques. A novel strategy to fuse the data at feature level and as well as at data level considers a defined fusion mechanism [3]. In Step2, a combination of potential sequences in the learning and search procedure is investigated. Data mining and knowledge discovery, using the refined data from the above for rule extraction and knowledge mining, with support for anomaly detection, pattern recognition and regression are also explored here [4]. In Step3, adaptation of knowledge representation approaches is achieved by combining different artificial intelligence methods [3] [4]. To provide decision support a hybrid approach is applied utilizing different machine learning algorithms, e.g. case-based reasoning, and clustering [4]. The approach offers several data analytics tasks, e.g. information fusion, anomaly detection, rules and knowledge extraction, clustering, pattern identification, correlation analysis, linear regression, logic regression, decision trees, etc. Thus, the approach assist in decision support, early detection of symptoms, context awareness and patient’s health status in a personal environment.

  • 15.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Intelligent Stress Management System2009Ingår i: Medicinteknikdagarna 2009, 2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, in our daily life we are subjected to a wide range of pressures. When the pressures exceed the extent that we are able to deal with then stress is trigged. High level of stress may cause serious health problems i.e. it reduces awareness of bodily symptoms. So, people may first notice it weeks or months later meanwhile the stress could cause more serious effect in the body and health. A difficult issue in stress management is to use biomedical sensor signals in the diagnosis and treatment of stress. This paper presents a case-based system that assists a clinician in diagnosis and treatment of stress. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques such as textual information retrieval, rule-based reasoning (RBR), and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with experts.

  • 16.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Kerstis, Birgitta
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Petrovic, Nikola
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sandborgh, Maria
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Third Eye: An Intelligent Assisting Aid for Visual Impairment Elderly2016Ingår i: Medicinteknikdagarna 2016 MTF, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Background Visually impaired older persons need support in daily activities, e.g. moving around inside the house; making and eating food and taking medicine independently. A system that simulates the environment based on both dynamic and static objects, identify obstacles, navigates and translates sensory information in voice would be valuable to support their daily activities. Today several sensors and camera-based systems are popular as ambient-assisted living tools for older adults. However, intelligent assisting aid (IAA) to support older individuals with a recently acquired visual impairment is limited. The proposed system ‘Third Eye’ focuses on the advanced research and development of an IAA to support older individuals with a recently acquired visual impairment. The main goal in this system is to provide a usable, feasible and cost-effective solution for older persons to support their daily activities using intelligent sensor based system. Method The system consists of the following five phases to meet several central challenges in developing IAA in such domain. • User-perspective, focuses on user-driven technical development, investigating needs of potential users. The study will have a participatory design with focus group interviews of lead users. • Sensor-based system, focuses on the identification obstacles based on ultrasounds and/or radio frequencies embedded in white-cane or weaker. • Camera-based system, focuses on image based information translation into voice embedded in white-cane or weaker or glasses. • System of systems, focuses on integration of above systems where knowledge is engineered and suitable representations are learned and reasoning for decisions are made [9]. • Experimental, focuses on usability and feasibility of the IAA, with idiographic and group studies Results The initial results have shown the necessity of the proposed AAI systems for older individuals with a recently acquired visual impairment. However, more extension work e.g., process and analyze the information and synthesize it with existing literature for developing the system is ongoing.

  • 17.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Björkman, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Causevic, Aida
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fotouhi, Hossein
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    An Overview on the Internet of Things for Health Monitoring Systems2016Ingår i: 2nd EAI International Conference on IoT Technologies for HealthCare HealthyIoT2015, 2016, Vol. 169, s. 429-436Konferensbidrag (Refereegranskat)
    Abstract [en]

    The aging population and the increasing healthcare cost in hospitals are spurring the advent of remote health monitoring systems. Advances in physiological sensing devices and the emergence of reliable low-power wireless network technologies have enabled the design of remote health monitoring systems. The next generation Internet, commonly referred to as Internet of Things (IoT), depicts a world populated by devices that are able to sense, process and react via the Internet. Thus, we envision health monitoring systems that support Internet connection and use this connectivity to enable better and more reliable services. This paper presents an overview on existing health monitoring systems, considering the IoT vision. We focus on recent trends and the development of health monitoring systems in terms of: (1) health parameters, (2) frameworks, (3) wireless communication, and (4) security issues. We also identify the main limitations, requirements and advantages within these systems.

  • 18.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Björkman, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Generic System-level Framework for Self-Serve Health Monitoring System through Internet of Things(IoT)2015Ingår i: Studies in Health Technology and Informatics, Volume 211: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, 2015, Vol. 211, s. 305-307Konferensbidrag (Refereegranskat)
    Abstract [en]

    Sensor data are traveling from sensors to a remote server, data is analysed remotely in a distributed manner, and health status of a user is presented in real-time. This paper presents a generic system-level framework for a self-served health monitoring system through the Internet of Things (IoT) to facilities an efficient sensor data management.

  • 19.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, Sweden.
    Espinosa, Jesica Rivero
    Technosite. Fundosa Group. R& D. Madrid, Spain.
    Reissner, Alenka
    Zveza Društev Upokojencev Slovenije Ljubljana, Slovenia.
    Domingo, Àlex
    Universitat Autònoma de Barcelona, Spain.
    Banaee, Hadi
    Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Rafael-Palou, Xavier
    Barcelona Digital Technology Centre Spain.
    Self-Serve ICT-based Health Monitoring to Support Active Ageing2015Ingår i: 8th International Conference on Health Informatics HEALTHINF, 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, the healthcare monitoring is not limited to take place in primary care facilities simply due to deployment of ICT. However, to support an ICT-based health monitoring, proper health parameters, sensor devices, data communications, approaches, methods and their combination are still open challenges. This paper presents a self-serve ICT-based health monitoring system to support active ageing by assisting seniors to participate in regular monitoring of elderly’s health condition. Here, the main objective is to facilitate a number of healthcare services to enable good health outcomes of healthy active living. Therefore, the proposed approach is identified and constructed three different kinds of healthcare services: 1) real time feedback generation service, 2) historical summary calculation service and 3) recommendation generation service. These services are implemented considering a number of health parameters, such as, 1) blood pressure, 2) blood glucose, 3) medication compliance, 4) weight monitoring, 5) physical activity, 6) pulse monitoring etc. The services are evaluated in Spain and Slovenia through 2 prototypical systems, i.e. year2prototype (Y2P) and year3prototype (Y3P) by 46 subjects (40 for Y2P and 6 for Y3P). The evaluation results show the necessity and competence of the proposed healthcare services. In addition, the prototypical system (i.e. Y3P) is found very much accepted and useful by most of the users.

  • 20.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fotouhi, Hossein
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Köckemann, Uwe
    Örebro University, Sweden.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Tomasic, Ivan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Run-Time Assurance for the E-care@home System2018Ingår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 107-110Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care@home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care@home.

  • 21.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Healthcare Service at Home: An Intelligent Health Monitoring System for Elderly2015Ingår i: Medicinteknikdagarna 2015 MFT 2015, 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents an intelligent healthcare service to support active ageing by assisting seniors to participate in regular monitoring of elderly’s health condition. The proposed system is applicable to use in home environment and offers a self-service approach to monitor elderly’s health condition. According to the evaluation, the proposed system shows its necessity, competence and usefulness.

  • 22.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Multi-parameter Sensing Platform in ESS-H and E-care@home2017Ingår i: Joint conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) EMBEC & NBC’17, 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Considering the population of ageing, health monitoring of elderly at home have the possibility for a person to keep track on his/her health status, e.g. decreased mobility in a personal environment. This also shows the potential of real-time decision support, early detection of symptoms, following of health trends and context awareness [1]. The ongoing projects Embedded Sensor for Health (ESS-H)1 and E-care@home2 are focusing on health monitoring of elderly at home. This paper presents the implementation of multi-parameter sensing on an Android platform. The objectives are, both to follow health trends and to enabling real time monitoring.

  • 23.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Rahman, Hamidur
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Quality index analysis on camera- A sed R-eak identification considering movements and light illumination2018Ingår i: Studies in Health Technology and Informatics, vol 249, IOS Press , 2018, s. 84-92Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: Normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men. 

  • 24.
    Akalin, Neziha
    et al.
    Örebro University, Sweden.
    Kristoffersson, Annica
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Loutfi, Amy
    Örebro University, Sweden.
    Evaluating the Sense of Safety and Security in Human–Robot Interaction with Older People2019Ingår i: Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction / [ed] Oliver Korn, Springer, Cham , 2019, s. 237-264Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    For many applications where interaction between robots and older people takes place, safety and security are key dimensions to consider. ‘Safety’ refers to a perceived threat of physical harm, whereas ‘security’ is a broad term which refers to many aspects related to health, well-being, and aging. This chapter presents a quantitative evaluation tool of the sense of safety and security for robots in elder care. By investigating the literature on measurement of safety and security in human–robot interaction, we propose new evaluation tools specially tailored to assess interaction between robots and older people.

  • 25.
    Akalin, Neziha
    et al.
    Örebro University, Sweden.
    Kristoffersson, Annica
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Loutfi, Amy
    Örebro University, Sweden.
    The Influence of Feedback Type in Robot-Assisted Training2019Ingår i: Multimodal Technologies and Interaction, E-ISSN 2414-4088, Vol. 4, nr 3, artikel-id 67Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Robot-assisted training, where social robots can be used as motivational coaches, provides an interesting application area. This paper examines how feedback given by a robot agent influences the various facets of participant experience in robot-assisted training. Specifically, we investigated the effects of feedback type on robot acceptance, sense of safety and security, attitude towards robots and task performance. In the experiment, 23 older participants performed basic arm exercises with a social robot as a guide and received feedback. Different feedback conditions were administered, such as flattering, positive and negative feedback. Our results suggest that the robot with flattering and positive feedback was appreciated by older people in general, even if the feedback did not necessarily correspond to objective measures such as performance. Participants in these groups felt better about the interaction and the robot.

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  • 26.
    Alexanderson, H.
    et al.
    Karolinska Univ Hosp, Karolinska Inst, Dept Neurobiol Care Sci & Soc, KI Physiotherapy Clin, Stockholm, Sweden..
    Björklund, Annika
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Hälsopoolen Rehabil Clin, Stockholm, Sweden..
    Ottosson, C.
    Karolinska Univ Hosp, Rheumatol Clin, Stockholm, Sweden..
    Dastmalchi, M.
    Karolinska Inst, Rheumatol Unit, Stockholm, Sweden..
    Lundberg, I. E.
    Karolinska Inst, Rheumatol Unit, Stockholm, Sweden..
    FATIGUE IN ADULT IDIOPATHIC INFLAMMATORY MYOPATHIES2015Ingår i: Annals of the Rheumatic Diseases, ISSN 0003-4967, E-ISSN 1468-2060, Vol. 74, s. 106-106Artikel i tidskrift (Övrigt vetenskapligt)
  • 27.
    Ask, P.
    et al.
    Department of Biomedical Engineering, Linköping University, Sweden.
    Ekstrand, K.
    ?.
    Hult, P.
    Department of Biomedical Engineering, Linköping University, Sweden.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Pettersson, N. -E
    Örebro County Council, Sweden.
    NovaMedTech - A regional program for supporting new medical technologies in personalized health care2012Ingår i: Studies in Health Technology and Informatics, 2012, s. 71-75Konferensbidrag (Refereegranskat)
    Abstract [en]

    NovaMedTech is an initiative funded from EU structural funds for supporting new medical technologies for personalized health care. It aims at bringing these technologies into clinical use and to the health care market. The program has participants from health care, industry and academia in East middle Sweden. The first three year period of the program was successful in terms of product concepts tried clinically, and number of products brought to a commercialization phase. Further, the program has led to a large number of scientific publications. Among projects supported, we can mention: Intelligent sensor networks; A digital pen to collect medical information about health status from patients; A web-based intelligent stethoscope; Methodologies to measure local blood flow and nutrition using optical techniques; Blood flow assessment from ankle pressure measurements; Technologies for pressure ulcer prevention; An IR thermometer for improved accuracy; A technique that identifies individuals prone to commit suicide among depressed patients; Detection of infectious disease using an electronic nose; Identification of the lactate threshold from breath; Obesity measurements using special software and MR camera; and An optical probe guided tumor resection. During the present three years period emphasis will be on entrepreneurial activities supporting the commercialization and bringing products to the market.

  • 28.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    GholamHosseini, Hamid
    Auckland University of Technology, Auckland, New Zealand.
    Connolly, M. J.
    University of Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Advanced decision support system for older adults2015Ingår i: Studies in Health Technology and Informatics, vol. 211, 2015, s. 235-240Konferensbidrag (Refereegranskat)
    Abstract [en]

    Decision support systems are rapidly becoming part of today's healthcare delivery. The paradigm has shifted from traditional and manual recording to computer-based electronic records and, further, to handheld devices as versatile and innovative healthcare monitoring systems. The current study focuses on interpreting multiple physical signs and early warning for hospitalized older adults so that severe consequences can be minimized. Data from a total of 30 patients have been collated in New Zealand Hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalized patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system is aimed to achieve a high level of agreement with clinicians' interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxemia, fever and hypothermia and to generate early warnings. 

  • 29.
    Baig, M. M.
    et al.
    Auckland University of Technology, New Zealand.
    GholamHosseini, Hamid
    Auckland University of Technology, New Zealand.
    Moqeem, A. A.
    Auckland University of Technology, New Zealand.
    Mirza, F.
    Auckland University of Technology, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption2017Ingår i: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 41, nr 7, artikel-id 115Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology. 

  • 30.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Hosseini, H. G.
    Auckland University of Technology, Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Machine learning-based clinical decision support system for early diagnosis from real-time physiological data2016Ingår i: Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc. , 2016, s. 2943-2946, artikel-id 7848584Konferensbidrag (Refereegranskat)
    Abstract [en]

    This research aims to design a self-organizing decision support system for early diagnosis of key physiological events. The proposed system consists of pre-processing, clustering and diagnostic system, based on self-organizing fuzzy logic modeling. The clustering technique was employed with empirical pattern analysis, particularly when the information available is incomplete or the data model is affected by vagueness, which is mostly the case with medical/clinical data. Clustering module can be viewed as unsupervised learning from a given dataset. This module partitions the patient vital signs to identify the key relationships, patterns and clusters among the medical data. Secondly, it uses self-organizing fuzzy logic modeling for early symptom and event detection. Based on the clustering outcome, when detecting abnormal signs, a high level of agreement was observed between system interpretation and human expert diagnosis of the physiological events and signs. © 2016 IEEE.

  • 31.
    Baig, Mirza Mansoor
    et al.
    Auckland Univ Technol, Auckland, New Zealand..
    GholamHosseini, Hamid
    Auckland Univ Technol, Auckland, New Zealand..
    Moqeem, Aasia A.
    Auckland Univ Technol, Auckland, New Zealand..
    Mirza, Farhaan
    Auckland Univ Technol, Auckland, New Zealand..
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges2019Ingår i: Health Informatics Journal, ISSN 1460-4582, E-ISSN 1741-2811, Vol. 25, nr 3, s. 1091-1104Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians' acceptability, as well as the low impact on the medical professionals' decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

  • 32.
    Baig, M.M.
    et al.
    Auckland University of Technology.
    GholamHosseini, Hamid
    Auckland University of Technology.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Tablet-based Patient Monitoring and Decision Support Systems in Hospital Care2015Ingår i: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2015, s. 1215-1218Konferensbidrag (Refereegranskat)
    Abstract [en]

    Remote patient monitoring with evidence-based decision support is revolutionizing healthcare. This novel approach could enable both patients and healthcare providers to improve quality of care and reduce costs. Clinicians can also view patients' data within the hospital network on tablet computers as well as other ubiquitous devices. Today, a wide range of applications are available on tablet computers which are increasingly integrating into the healthcare mainstream as clinical decision support systems. Despite the benefits of table-based healthcare applications, there are concerns around the accuracy, security and stability of such applications. In this study, we developed five tablet-based application screens for remote patient monitoring at hospital care settings and identified related issues and challenges. The ultimate aim of this research is to integrate decision support algorithms into the monitoring system in order to improve inpatient care and the effectiveness of such applications.

  • 33.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring2018Ingår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 26-33Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a distributed data analytics approach for drivers’ mental state monitoring using multivariate physiological signals. Driver’s mental states such as cognitive distraction, sleepiness, stress, etc. can be fatal contributing factors and to prevent car crashes these factors need to be understood. Here, a cloud-based approach with heterogeneous sensor sources that generates extremely large data sets of physiological signals need to be handled and analyzed in a big data scenario. In the proposed physiological big data analytics approach, for driver state monitoring, heterogeneous data coming from multiple sources i.e., multivariate physiological signals are used, processed and analyzed to aware impaired vehicle drivers. Here, in a distributed big data environment, multi-agent case-based reasoning facilitates parallel case similarity matching and handles data that are coming from single and multiple physiological signal sources.

  • 34.
    Baudin, Katarina
    et al.
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Gustafsson, Christine
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Frennert, Susanne
    Malmö University, Malmö, Sweden.
    Views of Swedish Elder Care Personnel on Ongoing Digital Transformation: Cross-Sectional Study2020Ingår i: Journal of Medical Internet Research, E-ISSN 1438-8871, Vol. 22, nr 6, artikel-id e15450Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Swedish municipalities are facing demographic challenges due to the growing number of older people and the resulting increased need for health care services. Welfare technologies are being launched as possible solutions for meeting some of these challenges.

    Objective: The aim of this study was to explore the perception, experimentation, evaluation, and procurement of welfare technology practices among professionals working in municipal elder care in relation to their gender, age, and profession.

    Methods: Data for this explorative cross-sectional study were collected from 393 responses to a web-based survey on municipal elder care in Sweden. Chi square tests were performed to determine the associations.

    Results: The results revealed gender, age, and professional differences in perspectives of municipal elder care workers. Differences were particularly evident in attitudes toward technology, both the use of technology in general and in the workplace, and involvement and participation in decision making regarding the procurement of new welfare technologies. Men (37/53, 70%) expressed a more positive attitude toward and curiosity regarding new technologies than women (157/336, 46.7%) (P=.03). Regarding age, the younger respondents (18-24 years old) perceived the digital transformation in the workplace as “too slow” (4/4, 100%), whereas the majority of older respondents (65-74 years old) perceived it as happening at the “right pace” (4/7, 57%). The elder care personnel felt encouraged by management to explore and experiment with new welfare technologies, but never did so either for management or with patients. Even though the majority of the respondents were women, more men (4/7, 57%) were involved in the procurement process for welfare technology devices and solutions than women (98/336, 29.2%) (P<.001).

    Conclusions: Personnel working within municipal elder care were generally very positive toward new technologies. However, both gender and age differences may influence these perspectives such as the personnel’s resistance to welfare technology and patients’ participation in welfare technology usage and deployment. Different levels of participation in the decision-making process regarding new technology deployment may negatively affect the overall digital transformation within municipal elder care.

  • 35.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Fusion Based System for Physiological Sensor Signal Classification2014Ingår i: Medicinteknikdagarna 2014 MTD10, 2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  • 36.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Kerstis, Birgitta
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Westerlund, Hanna
    Camanio Care AB, Sweden.
    Hjortsberg, Cecilia
    Västerås stad, Sweden.
    Food4You: A Personalized System for Adaptive Mealtime Situations for Elderly2017Ingår i: Medicinteknikdagarna 2017 MTD 2017, 2017Konferensbidrag (Refereegranskat)
  • 37.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik. Dalarna University, Borlänge, Sweden .
    Westin, Jerker
    Dalarna University, Borlänge, Sweden .
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Dougherty, Mark
    Dalarna University, Borlänge, Sweden .
    Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease: The 23rd Annual Workshop of the Swedish Artificial Intelligence Society Umeå, May 10-12, 20062006Ingår i: Proceedings of SAIS 2006, 2006, s. 67-72Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to "discover" relationships between fluctuation, treatment and disease severity in Parkinson. Preliminary results from this project are promising and show that Neuro-fuzzy techniques in combination with statistical methods may offer medical research and medical applications a useful combination of methods.

  • 38.
    Bengtsson, Marcus
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Elfving, Sofi
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Jackson, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik.
    The Factory-in-a-Box Concept and Its Maintenance Application2006Ingår i: Condition Monitoring and Diagnostic Engineering Managmement: Proceedings of the 19th International Congress on Condition Monitoring and Diagnostic Engineering Management / [ed] Kumar, Uday; Parida, Aditya; Rao, Raj BKN, Lulea, Sweden, 2006, s. 161-170Konferensbidrag (Refereegranskat)
    Abstract [en]

    The “Factory-in-a-Box” concept is currently being developed in a Swedish research project and consists of standardized production modules that are installed in a container and transported by e.g. a truck or by train. The modules may rapidly be combined into production systems that can be reconfigured for a new product and/or scaled to handle new volumes. The key features of this futuristic production system concept are thus flexibility, mobility, and speed. The objective with this paper is to discuss how research within the area of maintenance and condition monitoring will help realizing flexibility, mobility, and speed within the Factory-in-a-Box concept. The paper presents how maintenance will be an important enabler in the Factory-in-a-Box project and will explain and describe how maintenance should be used in achieving high availability.

  • 39.
    Bjonge, Ingrid Heien
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik.
    Holm, Jonathan Kenth
    Mälardalens universitet, Akademin för innovation, design och teknik.
    NEXT-GENERATION ARTIFICIAL HEART CONTROL: DEVELOPING AN INTELLIGENT CONTROL SYSTEM FOR OPTIMAL BLOOD FLOW AND PRESSURE IN A TOTAL ARTIFICIAL HEART2023Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Artificial hearts are an essential solution for patients suffering from end-stage heart failure. The precise control of these devices is critical for replicating the natural heart’s behavior and ensuring optimal patient health. This thesis presents the development and evaluation of control algorithms for a Total Artificial Heart (TAH). Our research initially considered the Proportional Integral Derivative (PID), Fuzzy-PID, and Artificial Neural Networks (ANN)-PID controllers. Through an iterative process of development and testing, two controllers emerged as the most effective: a Proportional (P) controller and a fuzzy Proportional Derivative (FPD) controller. These controllers were designed and simulated, followed by the generation of C code for implementation on an embedded system. An iterative approach was employed to design and test the controllers. First, the controllers were tested in a simulated environment, and then the validated designs were implemented and evaluated in a physical mock-loop system that mimicked the human circulatory system. The results demonstrated that both the P and FPD controllers were able to regulate the TAH operation. Notably, the FPD controller performs better based on the settling time, overshoot, and rise time in the simulation environment and stability in the physical environment. This thesis contributes to ongoing research in the field of TAH by providing a continuation that can advance the field’s development. These advancements could potentially improve the quality of life of patients awaiting heart transplants. Future work will include refining the FPD controller and conducting extensive physical testing and tuning.

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  • 40. Blobel, Bernd
    et al.
    Lindén, MariaMälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.Ahmed, Mobyen UddinMälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health: pHealth20152015Proceedings (redaktörskap) (Övrigt vetenskapligt)
  • 41.
    Brolin, Sandra
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Global Regulatory Requirements for Medical Devices2008Självständigt arbete på grundnivå (yrkesexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Medical devices are becoming more important in the health care sector. One of the major issues for companies developing and producing medical devices is to be updated on the regulatory requirements and implement them in the process. This thesis examines the regulatory requirements for medical devices in Argentina, Australia, Brazil, Canada, India, Japan, Mexico, Russia, South Korea and Taiwan and compares them with the requirements in the European Union.

    The conclusion of this thesis is that most countries have similar requirements for registration of medical devices and are striving to harmonize with the GHTF guidelines. A company goes far by following the requirements in EU, USA or the GHTF guidelines.

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  • 42.
    Bruch, Jessica
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Innovation och produktrealisering.
    Rösiö, Carin
    Mälardalens högskola, Akademin för innovation, design och teknik, Innovation och produktrealisering.
    Support for successful Production System Development: Handbook2015Rapport (Övrigt vetenskapligt)
    Abstract [en]

    For Swedish manufacturing companies active on the global market, high-performance production systems that contribute to the growth and competitiveness of the company are essential and one way to keep production in Sweden. Among a wide range of Swedish manufacturing companies it is becoming increasingly acknowledged that superior production system capabilities are crucial for competitive success. This being said, major attention has been paid to improving the operational performance of the production system. The focus in industry is mostly on the serial making of products, rather than on the prior development of the corresponding production system. At the end of the day, the real root cause of many problems and losses in production stem from issues that emanate from the development process of the production system. The potential of gaining a competitive edge by improving both the way the production system is developed and the way it is operated is hence ignored, even though it is a well-known fact that it is during the design phase that the most important decisions are made. In today’s industry, production system development is often still made ad hoc on the basis of past experiences and without any long-term perspective. If the production system is not designed in a proper way, it will eventually result in disturbances during both start-up and serial production. This leads to low capacity utilization, high production cost, and hence low profitability. To succeed, commitment is required as well as a shift in attention from the operations phase to the under-utilized potential of the design of production systems. The ideal outcome of production system development is the best possible production system that can easily be realized and is high-performing in operation. This will contribute to the growth and competitiveness of the company. To stay competitive, a shift in mind-set is required at many Swedish industries. Production system development is not only something that should work; it must be regarded as a competitive means and consequently be worked with systematically.

  • 43.
    Causevic, Aida
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fotouhi, Hossein
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lundqvist, Kristina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Data Security and Privacy in Cyber-Physical Systems for Healthcare2017Ingår i: Security and Privacy in Cyber-Physical Systems: Foundations, Principles, and Applications / [ed] Houbing Song D, Glenn A. Fink PhD, and Sabina Jeschke Dr. rer. nat., Wiley-IEEE Press , 2017, s. 305-320Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 44.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Motion Control i Västerås AB, Västerås.
    Gerdtman, C.
    Motion Control i Västerås AB, Västerås.
    Gharehbaghi, Arash
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A signal processing algorithm for improving the performance of a gyroscopic head-borne computer mouse2017Ingår i: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, s. 30-37Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a signal processing algorithm to remove different types of noise from a gyroscopic head-borne computer mouse. The proposed algorithm is a combination of a Kalman filter (KF), a Weighted-frequency Fourier Linear Combiner (WFLC) and a threshold with delay method (TWD). The gyroscopic head-borne mouse was developed to assist persons with movement disorders. However, since MEMS-gyroscopes are usually sensitive to environmental disturbances such as shock, vibration and temperature change, a large portion of noise is added at the same time as the head movement is sensed by the MEMS-gyroscope. The combined method is applied to the specially adapted mouse, to filter out different types of noise together with the offset and drift, with marginal need of the calculation capacity. The method is examined with both static state tests and movement operation tests. Angular position is used to evaluate the errors. The results demonstrate that the combined method improved the head motion signal substantially, with 100.0% error reduction during the static state, 98.2% position error correction in the case of movements without drift and 99.9% with drift. The proposed combination in this paper improved the static stability and position accuracy of the gyroscopic head-borne mouse system by reducing noise, offset and drift, and also has the potential to be used in other gyroscopic sensor systems to improve the accuracy of signals. 

  • 45.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gerdtman, Christer
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Development of a MEMS-sensor based motion analysis system for human movement rehabilitation2017Ingår i: International conference on movement: brain, body, cognition Movement2017, 2017Konferensbidrag (Refereegranskat)
  • 46.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gerdtman, Christer
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Signal processing to improve the MEMS sensor signal in a small embedded sensor system for health2017Ingår i: Medicinteknikdagarna 2017 MTD 2017, 2017Konferensbidrag (Refereegranskat)
  • 47.
    Ebrahimi, Zahra
    et al.
    Shahrood University of Technology, Shahroud, Iran.
    Loni, Mohammad
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Daneshtalab, Masoud
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ghareh Baghi, Arash
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Review on Deep Learning Methods for ECG Arrhythmia Classification2020Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 7, artikel-id 100033Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively

  • 48.
    Ehn, Maria
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Carlén Eriksson, Lennie
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Åkerberg, Nina
    Västerås Municipality, Västerås, Sweden.
    Johansson, Ann-Christin
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Activity Monitors as Support for Older Persons’ Physical Activity in Daily Life: Qualitative Study of the Users’ Experiences2018Ingår i: JMIR mhealth and uhealth, E-ISSN 2291-5222, Vol. 6, nr 2, artikel-id e34Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background

    Falls are a major threat to the health and independence of seniors. Regular physical activity (PA) can prevent 40% of all fall injuries. The challenge is to motivate and support seniors to be physically active. Persuasive systems can constitute valuable support for persons aiming at establishing and maintaining healthy habits. However, these systems need to support effective behavior change techniques (BCTs) for increasing older adults’ PA and meet the senior users’ requirements and preferences. Therefore, involving users as codesigners of new systems can be fruitful. Prestudies of the user’s experience with similar solutions can facilitate future user-centered design of novel persuasive systems.

    Objective

    The aim of this study was to investigate how seniors experience using activity monitors (AMs) as support for PA in daily life. The addressed research questions are as follows: (1) What are the overall experiences of senior persons, of different age and balance function, in using wearable AMs in daily life?; (2) Which aspects did the users perceive relevant to make the measurements as meaningful and useful in the long-term perspective?; and (3) What needs and requirements did the users perceive as more relevant for the activity monitors to be useful in a long-term perspective?

    Methods

    This qualitative interview study included 8 community-dwelling older adults (median age: 83 years). The participants’ experiences in using two commercial AMs together with tablet-based apps for 9 days were investigated. Activity diaries during the usage and interviews after the usage were exploited to gather user experience. Comments in diaries were summarized, and interviews were analyzed by inductive content analysis.

    Results

    The users (n=8) perceived that, by using the AMs, their awareness of own PA had increased. However, the AMs’ impact on the users’ motivation for PA and activity behavior varied between participants. The diaries showed that self-estimated physical effort varied between participants and varied for each individual over time. Additionally, participants reported different types of accomplished activities; talking walks was most frequently reported. To be meaningful, measurements need to provide the user with a reliable receipt of whether his or her current activity behavior is sufficient for reaching an activity goal. Moreover, praise when reaching a goal was described as motivating feedback. To be useful, the devices must be easy to handle. In this study, the users perceived wearables as easy to handle, whereas tablets were perceived difficult to maneuver. Users reported in the diaries that the devices had been functional 78% (58/74) of the total test days.

    Conclusions

    Activity monitors can be valuable for supporting seniors’ PA. However, the potential of the solutions for a broader group of seniors can significantly be increased. Areas of improvement include reliability, usability, and content supporting effective BCTs with respect to increasing older adults’ PA.

  • 49.
    Ehn, Maria
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Hansson, P.
    SICS Swedish ICT, Kista, Sweden.
    Sjölinder, M.
    SICS Swedish ICT, Kista, Sweden.
    Boman, I. -L
    Karolinska Institutet, Solna, Sweden .
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sommerfeld, D.
    SICS Swedish ICT, Kista, Sweden.
    Borg, J.
    SICS Swedish ICT, Kista, Sweden.
    Palmcrantz, S.
    SICS Swedish ICT, Kista, Sweden.
    Users perspectives on interactive distance technology enabling home-based motor training for stroke patients2015Ingår i: Studies in Health Technology and Informatics, vol. 211, 2015, s. 145-152Konferensbidrag (Refereegranskat)
    Abstract [en]

    The aim of this work has been to develop a technical support enabling home-based motor training after stroke. The basis for the work plan has been to develop an interactive technical solution supporting three different groups of stroke patients: (1) patients with stroke discharged from hospital with support from neuro team; (2) patients with stroke whose support from neuro team will be phased out and (3) patients living with impaired motor functions long-term. The technology has been developed in close collaboration with end-users using a method earlier evaluated and described [12]. This paper describes the main functions of the developed technology. Further, results from early user-tests with end-users, performed to identify needs for improvements to be carried out during further technical development. The developed technology will be tested further in a pilot study of the safety and, usefulness of the technology when applied as a support for motor training in three different phases of the post-stroke rehabilitation process. 

  • 50.
    Ehn, Maria
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Clinical sensor-based fall risk assessment at an orthopedic clinic: A case study of the staff’s views on utility and effectiveness2023Ingår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 4, artikel-id 1904Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

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