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  • 1.
    Abbaspour Asadollah, Sara
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. RISE Acreo AB, Sweden.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    GholamHosseini, Hamid
    Auckland University of Technology, Auckland, New Zealand.
    Naber, A.
    Chalmers University of Technology, Gothenburg, Sweden.
    Ortiz-Catalan, M.
    Chalmers University of Technology, Gothenburg, Sweden.
    Evaluation of surface EMG-based recognition algorithms for decoding hand movements2020Ingår i: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 58, nr 1, s. 83-100Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.

  • 2.
    Abbaspour Gildeh, Saedeh
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fotouhi, Faranak
    Fotouhi, Hossein
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Vahabi, Maryam
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Deep learning-based motion activity recognition using smartphone sensors2020Ingår i: 12th International Conference on e-Health e-Health'20, 2020Konferensbidrag (Refereegranskat)
  • 3.
    Abbaspour, Sara
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fallah, Ali
    Amirkabir University of Technology, Tehran, Iran.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    GholamHosseini, Hamid
    Auckland University of Technology, Auckland, New Zealand.
    A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet2016Ingår i: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, Vol. 26, s. 52-59Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).

  • 4.
    Abbaspour, Sara
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Engineering Department, University of Qom, Iran.
    Fotouhi, F.
    Engineering Department, University of Qom, Iran.
    Sedaghatbaf, A.
    RISE Research Institutes of Sweden, Sweden.
    Fotouhi, Hossein
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Vahabi, Maryam
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. ABB Corporate Research, Sweden.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A comparative analysis of hybrid deep learning models for human activity recognition2020Ingår i: Sensors, E-ISSN 1424-8220, Vol. 20, nr 19, s. 1-14, artikel-id 5707Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

  • 5.
    Abbaspour, Sara
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    GholamHosseini, Hamid
    Mälardalens högskola, Akademin för innovation, design och teknik. School of Engineering, Auckland University of TechnologyAuckland, New Zealand .
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Evaluation of wavelet based methods in removing motion artifact from ECG signal2015Ingår i: IFMBE Proceedings, 2015, s. 1-4Konferensbidrag (Refereegranskat)
    Abstract [en]

    Accurate recording and precise analysis of the electrocardiogram (ECG) signals are crucial in the pathophysiological study and clinical treatment. These recordings are often corrupted by different artifacts. The aim of this study is to propose two different methods, wavelet transform based on nonlinear thresholding and a combination method using wavelet and independent component analysis (ICA), to remove motion artifact from ECG signals. To evaluate the performance of the proposed methods, the developed techniques are applied to the real and simulated ECG data. The results of this evaluation are presented using quantitative and qualitative criteria. The results show that the proposed methods are able to reduce motion artifacts in ECG signals. Signal to noise ratio (SNR) of the wavelet technique is equal to 13.85. The wavelet-ICA method performed better with SNR of 14.23.

  • 6.
    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)
  • 7.
    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.
    GholamHosseini, Hamid
    Auckland University of Technology, New Zealand.
    ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA2015Ingår i: Studies in Health Technology and Informatics, Volume 211, 2015, s. 91-97Konferensbidrag (Refereegranskat)
    Abstract [en]

    This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.

  • 8.
    Abbaspour, Sara
    et al.
    Massachusetts Gen Hosp, Dept Neurol, Boston, MA 02114 USA.;Harvard Med Sch, Div Sleep Med, Boston, MA 02114 USA..
    Naber, Autumn
    Ctr Bion & Pain Res, S-43180 Molndal, Sweden.;Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden..
    Ortiz-Catalan, Max
    Ctr Bion & Pain Res, S-43180 Molndal, Sweden.;Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden.;Sahlgrens Univ Hosp, Operat Area 3, S-43180 Molndal, Sweden.;Univ Gothenburg, Sahlgrenska Acad, Inst Clin Sci, Dept Orthopaed, S-43180 Molndal, Sweden..
    GholamHosseini, Hamid
    Auckland Univ Technol, Dept Elect & Elect Engn, Auckland 1010, New Zealand..
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements2021Ingår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 16, artikel-id 5677Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

  • 9.
    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.

  • 10.
    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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  • 11. Abdul-Ahad, Amir Stefan
    et al.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Larsson, Thomas
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Mahmoud, Waleed A.
    Robust Distance-Based Watermarking for Digital Video2008Ingår i: Proceedings of The Annual SIGRAD Conference, Stockholm, 2008Konferensbidrag (Refereegranskat)
  • 12.
    Abdul-Ahad, Amir Stefan
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Çürüklü, Baran
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Indirect Wavelet-Based Cardio Arrhythmia Detection Algorithm2008Ingår i: Medicinteknikdagarna, Gothenburg, Sweden, 2008, s. 14-15Konferensbidrag (Refereegranskat)
  • 13.
    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.

  • 14.
    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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  • 15.
    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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  • 16.
    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.

  • 17.
    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.

  • 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.
    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.

  • 19.
    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.

  • 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.
    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.

  • 24. Ask, Per
    et al.
    Ekstrand, Kristina
    Hult, Peter
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Pettersson, Nils-Erik
    A regional program for supporting new medical technologies in personalized health care2012Ingår i: PHealth 2012, 2012, s. 71-75Konferensbidrag (Refereegranskat)
  • 25.
    Ask, Per
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Hult, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Pettersson, Nils-Erik
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ekstrand, Kristina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    NovaMedTechs satsning pa medicinsk teknik for individualiserad vard2011Ingår i: Svenska Lakaresallskapets riksstamma 2011, 30 Nov-2 Dec, Stockholm, Sweden, 2011Konferensbidrag (Refereegranskat)
  • 26.
    Baig, M. M.
    et al.
    Data Science Team, Orion Health, Auckland, New Zealand.
    Gholam Hosseini, H.
    Auckland University of Technology, Auckland, New Zealand.
    Afifi, S.
    Auckland University of Technology, Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A systematic review of rapid response applications based on early warning score for early detection of inpatient deterioration2021Ingår i: Informatics for Health and Social Care, ISSN 1753-8157, E-ISSN 1753-8165, Vol. 46, nr 2, s. 148-157Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Aim: The aim of this study was to investigate the effectiveness of current rapid response applications available in acute care settings for escalation of patient deterioration. Current challenges and barriers, as well as key recommendations, were also discussed. Methods: We adopted PRISMA review methodology and screened a total of 559 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2015 and 2019. The selection criteria were based on the inclusion of studies that report on the advancement made to the current practice for providing rapid response to the patient deterioration in acute care settings. Results: We found that current rapid response applications are complicated and time-consuming for detecting inpatient deterioration. Existing applications are either siloed or challenging to use, where clinicians are required to move between two or three different applications to complete an end-to-end patient escalation workflow–from vital signs collection to escalation of deteriorating patients. We found significant differences in escalation and responses when using an electronic tool compared to the manual approach. Moreover, encouraging results were reported in extensive documentation of vital signs and timely alerts for patient deterioration. Conclusion: The electronic vital signs monitoring applications are proved to be efficient and clinically suitable if they are user-friendly and interoperable. As an outcome, several key recommendations and features were identified that would be crucial to the successful implementation of any rapid response system in all clinical settings.

  • 27.
    Baig, M. M.
    et al.
    School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand.
    Gholamhosseini, H.
    School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand.
    Gutierrez, J.
    School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand.
    Ullah, E.
    School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications2021Ingår i: Applied Clinical Informatics, ISSN 1869-0327, Vol. 12, nr 1, s. 1-9, artikel-id 200124raArtikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. Objectives This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. Methods We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. Results The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. Conclusion We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice. 

  • 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.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    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.
    Diagnosis and Biofeedback System for Stress2009Ingår i: Proceedings of the 6th International Workshop on Wearable, Micro, and Nano Technologies for Personalized Health: "Facing Future Healthcare Needs", pHealth 2009, 2009, s. 17-20Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, everyday life for many people contain many situations that may trigger stress or result in an individual living on an increased stress level under long time. High level of stress may cause serious health problems. It is known that respiratory rate is an important factor and can be used in diagnosis and biofeedback training, but available measurement of respiratory rate are not especially suitable for home and office use. The aim of this project is to develop a portable sensor system that can measure the stress level, during everyday situations e.g. at home and in work environment and can help the person to change the behaviour and decrease the stress level. The sensor explored is a finger temperature sensor. Clinical studies show that finger temperature, in general, decreases with stress; however this change pattern shows large individual variations. Diagnosing stress level from the finger temperature is difficult even for clinical experts. Therefore a computer-based stress diagnosis system is important. In this system, case-based reasoning and fuzzy logic have been applied to assists in stress diagnosis and biofeedback treatment utilizing the finger temperature sensor signal. An evaluation of the system with an expert in stress diagnosis shows promising result.

  • 34.
    Bergblomma, Marcus
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ekström, Martin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Björkman, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ekström, Mikael
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Gerdtman, Christer
    Motion Control AB, Västerås, Sweden .
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A wireless low latency control system for harsh environments2012Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline): Vol. 11, PART 1, 2012, s. 17-22Konferensbidrag (Refereegranskat)
    Abstract [en]

    The use of wireless communication technologies in the industry offer severaladvantages. One advantage is the ability to deploy sensors where they previously could noteasily be deployed, for instance on parts that rotate. To use wireless communication in industrialcontrol loops, demands on reliability and latency requirements has to be met. This in anenvironment that may be harsh for radio communication. This work presents a reliable, lowlatency wireless communication system. The system is used in a wireless thyristor control loopin a hydro power plant generator. The wireless communication is based on Bluetooth radiomodules. The work shows a latency analysis together with empirical hardware based latencyand packet error rate measurements. The background noise of a hydro power plant station isalso investigated. The average latency between the Bluetooth modules for the proposed systemis 5.09 ms. The packet error rate is 0.00288 for the wireless low latency control system deployedin a hydro power plant.

  • 35.
    Bergblomma, Marcus
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ekström, Martin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ekström, Mikael
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Garcia Castaño, Javier
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Björkman, Mats
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Wireless ECG network2009Ingår i: WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, VOL 25, PT 5, 2009, s. 244-247Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a time synchronized wireless ECG sensor network with reliable data communication. Wireless ECG systems are a popular research area where several research groups have presented point-to-point solutions. Alongside the wireless ECG research, the wireless sensor network research has created an increasing interest for secure, low power and predictable network applications. Combining these research areas is a natural step for the evolution of secure wireless monitoring of physiological parameters. In this study the Bluetooth radio standard has been chosen for its versatility. This paper focuses on both the hardware and the software development for a functional multihop ECG network using Bluetooth. The presented wireless ECG network is reliable up to link loss and is easily configured to send more or different types of signals. The system has been tested and verified for secure multihop communication.

  • 36.
    Berglin, Lena
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ekström, Mikael
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Monitoring health and activity by smartwear2005Konferensbidrag (Övrigt vetenskapligt)
  • 37.
    Bergstrand, Sara
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ek, Anna-Christina
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindberg, Lars-Göran
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Länne, Torste
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindgren, Margareta
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Tissue blood flow response to external pressure in the sacral region using PPG and laser Doppler technique2009Ingår i: 12th Annual European Pressure Ulcer Advisory Panel Open Meeting in Amsterdam, 3rd to 5th September 2009, Netherlands, 2009Konferensbidrag (Refereegranskat)
  • 38. Bergstrand, Sara
    et al.
    Lanne, Torste
    Ek, Anna-Christina
    Lindberg, Lars-Göran
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindgren, Margareta
    Existence of Tissue Blood Flow in Response to External Pressure in the Sacral Region of Elderly Individuals: Using an Optical Probe Prototype2010Ingår i: Microcirculation, ISSN 1073-9688, E-ISSN 1549-8719, Vol. 17, nr 4, s. 311-319Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    OBJECTIVE: The aim was to investigate the existence of sacral tissue blood flow at different depths in response to external pressure and compression in elderly individuals using a newly developed optical probe prototype. METHODS: The tissue blood flow and tissue thickness in the sacral area were measured during load in 17 individuals using laser Doppler flowmetry and photoplethysmography in a combined probe, and digital ultrasound. RESULTS: The mean age was 68.6 +/- 7.0 years. While loading, the mean compression was 60.3 +/- 11.9%. The number of participants with existing blood flow while loading increased with increased measurement depth. None had enclosed blood flow deep in the tissue and at the same time an existing more superficial blood flow. Correlation between tissue thickness and BMI in unloaded and loaded sacral tissue was shown: r = 0.68 (P = 0.003) and r = 0.68 (P = 0.003). CONCLUSIONS: Sacral tissue is highly compressed by external load. There seems to be a difference in responses to load in the different tissue layers, as occluded blood flow in deeper tissue layers do not occur unless the blood flow in the superficial tissue layers is occluded.

  • 39. Bergstrand, Sara
    et al.
    Lindberg, Lars-Göran
    Ek, Anna-Christina
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindgren, Margareta
    Blood flow measurements at different depths using photoplethysmography and laser Doppler techniques2009Ingår i: Skin research and technology, ISSN 0909-752X, E-ISSN 1600-0846, Vol. 15, s. 139-147Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study has evaluated a multi-parametric system combining laser Doppler flowmetry and photoplethysmography in a single probe for the simultaneous measurement of blood flow at different depths in the tissue. This system will be used to facilitate the understanding of pressure ulcer formation and in the evaluation of pressure ulcer mattresses.

    The blood flow in the tissue over the sacrum was measured before, during and after loading with 37.5 mmHg, respectively, 50.0 mmHg. The evaluation of the system consisted of one clinical part, and the other part focusing on the technicalities of the probe prototype.

    An increase in blood flow while loading was the most common response, but when the blood flow decreased during loading it was most affected at the skin surface and the blood flow responses may be different due to depths of measurement. Reactive hyperaemia may occur more frequently in the superficial layers of the tissue.

    The study showed that the new system is satisfactory for measuring tissue blood flow at different depths. The laser Doppler complements the photoplethysmography, and further development of the system into a thin flexible probe with the ability to measure a larger area is required.

  • 40.
    Björkman, Mats
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. ES (Embedded Systems).
    Cooperation between academia and industry within embedded sensor systems2018Ingår i: World Congress on Medical Physics and Biomedical Engineering IUPESM 2018, 2018, Vol. 68, nr 1Konferensbidrag (Refereegranskat)
  • 41.
    Björkman, Mats
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Trådlösa sensornät i vård och omsorg2009Ingår i: Medicinteknikdagarna 2009, 2009Konferensbidrag (Refereegranskat)
  • 42. 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)
  • 43.
    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. 

  • 44.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gerdtman, C.
    Motion Control i Västerås AB, Västerås, Sweden .
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Signal processing algorithms for position measurement with MEMS-based accelerometer2015Ingår i: IFMBE Proceedings, vol. 48, 2015, s. 36-39Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents signal processing algorithms for position measurements with MEMS-accelerometers in a motion analysis system. The motion analysis system is intended to analyze the human motion with MEMS-based-sensors which is a part of embedded sensor systems for health. MEMS-accelerometers can be used to measure acceleration and theoretically the velocity and position can be derived from the integration of acceleration. However, there normally is drift in the measured acceleration, which is enlarged under integration. In this paper, the signal processing algorithms are used to minimize the drift during integration by MEMS-based accel-erometer. The simulation results show that the proposed algorithms improved the results a lot. The algorithm reduced the drift in one minute by about 20 meters in the simulation. It can be seen as a reference of signal processing for the motion analysis system with MEMS-based accelerometer in the future work.

  • 45.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gerdtman, C.
    Motion Control i Vasteras AB, Vasteras, Sweden .
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Signal processing algorithms for temperauture drift in a MEMS-gyro-based head mouse2014Ingår i: Int. Conf. Syst. Signals Image Process., 2014, s. 123-126Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a comparison between different signal processing algorithms applied to a gyro-based computer head mouse for persons with movement disorders. MEMS-gyros can be used to sense the head movement and rotation. However, the measured gyro signals are influenced by noise, offset, drift and especially temperature drift. Thus, there is a need to improve the signal by signal processing algorithms. Different gyros have different characteristics and the algorithms should be useful for any selected MEMS-gyro. In this paper, three different signal processing algorithms were designed and evaluated by simulation in MATLAB and implementation in a dsPIC, with the aim to compensate for the temperature drift problem. The algorithms are high-pass filtering, Kalman algorithm and Least Mean Square (LMS) algorithm. Comparisons and system test show that these filters can be used for temperature drift compensation and the Kalman filter showed the best in the application of a MEMS-gyro-based computer head mouse.

  • 46.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Motion Control i Västerås AB, Västerås, Sweden.
    Gerdtman, C.
    Motion Control i Västerås AB, Västerås, Sweden.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems: A systematic review2018Ingår i: Sensors, E-ISSN 1424-8220, Vol. 18, nr 4, artikel-id 1123Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented.

  • 47.
    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)
  • 48.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gerdtman, Christer
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Motion Control i Västerås AB.
    Noise reduction for a MEMS-­gyroscope-­based head mouse2015Ingår 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, Västerås, Sweden: IOS Press , 2015, s. 98-104Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, four different signal processing algorithms which can be applied to reduce the noise from a MEMS-gyroscope-based computer head mouse are presented. MEMS-gyroscopes are small, light, cheap and widely used in many electrical products. MultiPos, a MEMS-gyroscope-based computer head mouse system was designed for persons with movement disorders. Noise such as physiological tremor and electrical noise is a common problem for the MultiPos system. In this study four different signal processing algorithms were applied and evaluated by simulation in MATLAB and implementation in a dsPIC, with aim to minimize the noise in MultiPos. The algorithms were low-pass filter, Least Mean Square (LMS) algorithm, Kalman filter and Weighted Fourier Linear Combiner (WFLC) algorithm. Comparisons and system tests show that these signal processing algorithms can be used to improve the MultiPos system. The WFLC algorithm was found the best method for noise reduction in the application of a MEMS-gyroscope-based head mouse.

  • 49.
    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)
  • 50.
    Du, Jiaying
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Kade, Daniel
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Motion Control i Västerås AB, Västerås, Sweden.
    Gerdtman, Christer
    Motion Control i Västerås AB, Västerås, Sweden.
    Lindell, Rikard
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ozcan, Oguzhan
    Arçelik Research Center for Creative Industries, Koç University, Rumelifeneri, Sarıyer, İstanbul, Turkey.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems2016Ingår i: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, Västeraås, Sweden, 2016, Vol. 187, s. 107-112Konferensbidrag (Refereegranskat)
    Abstract [en]

    New computer input devices in healthcare applications using small embedded sensors need firmware filters to run smoothly and to provide a better user experience. Therefore, it has to be investigated how much delay can be tolerated for signal processing before the users perceive a delay when using a computer input device. This paper is aimed to find out a threshold of unperceived delay by performing user tests with 25 participants. A communication retarder was used to create delays from 0 to 100 ms between a receiving computer and three different USB-connected computer input devices. A wired mouse, a wifi mouse and a head-mounted mouse were used as input devices. The results of the user tests show that delays up to 50ms could be tolerated and are not perceived as delay, or depending on the used device still perceived as acceptable.

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