https://www.mdu.se/

mdu.sePublications
Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Estimating Physiological Parameters in Various Age Groups: Windkessel 4 Element Model and PPG Waveform Analysis Approach2023In: IEEE 4th International Multidisciplinary Conference on Engineering Technology, IMCET 2023, IEEE, 2023, p. 194-197Conference paper (Refereed)
    Abstract [en]

    Non-invasive monitoring of cardiovascular health through photoplethysmography (PPG) waveforms has emerged as a crucial area of research. The Windkessel 4-Element (WK4) model is a mathematical approach used to estimate key physiological parameters related to cardiovascular health, including arterial compliance, peripheral resistance, inertance, and total arterial resistance. This study aimed to evaluate key physiological parameters associated with cardiovascular health using the WK4 model, leveraging real-life PPG waveform data obtained from volunteers across three distinct age groups. To achieve this, an algorithm was developed to automatically determine optimal parameter values for each volunteer. The results revealed a mean correlation coefficient of 0.96 between the automatically generated waveforms by the algorithm and the actual real-life PPG waveforms, indicating robust agreement. Notably, only the total arterial resistance parameter exhibited significant differences among the age groups, suggesting that the algorithm holds promise for detecting agerelated changes in cardiovascular health. These findings emphasize the potential for the development of a non-invasive tool to assess cardiovascular health status and enhance healthcare outcomes. Furthermore, they underscore the capability of the developed algorithm as a non-invasive means to evaluate various aspects of cardiovascular physiology. Additionally, the versatility of this algorithm opens doors for its application in educational settings, promoting knowledge advancement, empowering research endeavors, and facilitating advancements in the field.

  • 2.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring2023Conference paper (Refereed)
    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.

  • 3.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    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
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 5, article id 2758Article, review/survey (Refereed)
    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.

    Download full text (pdf)
    fulltext
  • 4.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden.
    Gunnarsson, Emanuel
    Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden.
    Ramos, Alberto
    Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden;UDIT—University of Design, Innovation and Technology, 28016 Madrid, Spain.
    Rödby, Kristian
    Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden.
    Abtahi, Farhad
    Institute for Clinical Science, Intervention and Technology, Karolinska Institutet, 141 83 Stockholm, Sweden;Department of Medical Care Technology, Karolinska University Hospital, 141 57 Huddinge, Sweden;Department of Clinical Physiology, Karolinska University Hospital, 141 57 Huddinge, Sweden.
    Bamidis, Panagiotis D.
    Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.
    Billis, Antonis
    Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.
    Papachristou, Panagiotis
    Academic Primary Health Care Center, Region Stockholm, 104 31 Stockholm, Sweden;Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, Sweden.
    Seoane, Fernando
    Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden;Institute for Clinical Science, Intervention and Technology, Karolinska Institutet, 141 83 Stockholm, Sweden;Department of Medical Care Technology, Karolinska University Hospital, 141 57 Huddinge, Sweden;Department of Clinical Physiology, Karolinska University Hospital, 141 57 Huddinge, Sweden.
    Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 22, p. 9208-9208Article in journal (Refereed)
    Abstract [en]

    The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

    Download full text (pdf)
    fulltext
  • 5.
    Abdelakram, Hafid
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdullah, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Edu-Mphy: A Low-Cost Multi-Physiological Recording System for Education and Research in Healthcare and Engineering2023In: Abstracts: Medicinteknikdagarna 2023, 2023, p. 117-117Conference paper (Other academic)
    Download full text (pdf)
    fulltext
  • 6.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Department of Biomedical Engineering, Riphah International University, Lahore, Pakistan.
    Abdelakram, Hafid
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    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 Detection2023In: Abstracts: Medicinteknikdagarna 2023, 2023, p. 118-118Conference paper (Other academic)
    Download full text (pdf)
    fulltext
  • 7.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Abdelakram, Hafid
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters2023In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 923-924Conference paper (Refereed)
    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.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hafid, Abdelakram
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kristoffersson, Annica
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points2023In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, article id 1199604Article in journal (Refereed)
    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.

    Download full text (pdf)
    fulltext
  • 9.
    Abdullah, Saad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hafid, Abdelakram
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Shahid, H.
    Coventry University, Research Centre for Intelligent Healthcare, Coventry, United Kingdom.
    Comparing the Effectiveness of EMG and Electrical Impedance myography Measurements for Controlling Prosthetics2023In: IEEE Int. Multidiscip. Conf. Eng. Technol., IMCET, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 189-193Conference paper (Refereed)
    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.

1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf