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Abdullah, S., Hafid, A., Folke, M., Lindén, M. & Kristoffersson, A. (2023). A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD). Electronics, 12(5), Article ID 1174.
Open this publication in new window or tab >>A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)
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2023 (English)In: Electronics, E-ISSN 2079-9292, Vol. 12, no 5, article id 1174Article in journal (Refereed) Published
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.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-62004 (URN)10.3390/electronics12051174 (DOI)000947098400001 ()2-s2.0-85149747017 (Scopus ID)
Available from: 2023-03-03 Created: 2023-03-03 Last updated: 2023-04-12Bibliographically approved
Abdelakram, H., Abdullah, S., Lindén, M., Kristoffersson, A. & Folke, M. (2023). Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring. In: : . Paper presented at 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 22-24 June 2023, L'Aquila, Italy.
Open this publication in new window or tab >>Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring
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2023 (English)Conference paper, Published 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.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-64033 (URN)10.1109/CBMS58004.2023.00316 (DOI)001037777900135 ()2-s2.0-85166470920 (Scopus ID)979-8-3503-1224-9 (ISBN)
Conference
2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 22-24 June 2023, L'Aquila, Italy
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-10-25Bibliographically approved
Abdullah, S., Abdelakram, H., Lindén, M., Folke, M. & Kristoffersson, A. (2023). Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems: . Paper presented at 36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, Aquila, 22 June 2023 through 24 June 2023 (pp. 923-924). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters
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2023 (English)In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 923-924Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
acceleration photoplethysmography, cardiovascular, fiducial points, hypertension, PPG, Acceleration, Classification (of information), Learning systems, Statistical tests, Support vector machines, Cardiovascular disease, Causes of death, Clinical parameters, Machine-learning, Photoplethysmogram, Photoplethysmography
National Category
Cardiac and Cardiovascular Systems Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-63964 (URN)10.1109/CBMS58004.2023.00344 (DOI)001037777900162 ()2-s2.0-85166469701 (Scopus ID)9798350312249 (ISBN)
Conference
36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, Aquila, 22 June 2023 through 24 June 2023
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-10-25Bibliographically approved
Abdullah, S., Hafid, A., Folke, M., Lindén, M. & Kristoffersson, A. (2023). PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points. Frontiers in Bioengineering and Biotechnology, 11, Article ID 1199604.
Open this publication in new window or tab >>PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points
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2023 (English)In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, article id 1199604Article in journal (Refereed) Published
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.

Keywords
photoplethysmography, PPG features, fiducial points, MATLAB, toolbox, signal processing, acceleration photoplethysmography, velocity photoplethysmography
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-63035 (URN)10.3389/fbioe.2023.1199604 (DOI)001020124900001 ()2-s2.0-85163601193 (Scopus ID)
Available from: 2023-06-09 Created: 2023-06-09 Last updated: 2023-07-26Bibliographically approved
Abdelakram, H., Difallah, S., Alves, C., Abdullah, S., Folke, M., Lindén, M. & Kristoffersson, A. (2023). State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review. Sensors, 23(5), Article ID 2758.
Open this publication in new window or tab >>State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review
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2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 5, article id 2758Article, review/survey (Refereed) Published
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.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-62006 (URN)10.3390/s23052758 (DOI)000947664800001 ()36904965 (PubMedID)2-s2.0-85149769899 (Scopus ID)
Available from: 2023-03-03 Created: 2023-03-03 Last updated: 2023-05-10Bibliographically approved
Kristoffersson, A., Åkerberg, A. & Folke, M. (2023). Towards providing type-2 diabetes patients with homogenous information on physical activity. In: Abstracts: Medicinteknikdagarna 2023. Paper presented at Medicinteknikdagarna 2023, Stockholm, Sweden, 9-11 oktober, 2023 (pp. 54-54).
Open this publication in new window or tab >>Towards providing type-2 diabetes patients with homogenous information on physical activity
2023 (English)In: Abstracts: Medicinteknikdagarna 2023, 2023, p. 54-54Conference paper, Oral presentation with published abstract (Other academic)
National Category
Computer Systems Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-64769 (URN)
Conference
Medicinteknikdagarna 2023, Stockholm, Sweden, 9-11 oktober, 2023
Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2023-11-17Bibliographically approved
Folke, M., Åkerberg, A., Arkkukangas, M., Kärnsund, A. & Johnsson, M. (2020). Evaluation of the content of a web tool aimed to identify early markers related to fall risk among middle-aged people. Health and Technology, 1571-1578
Open this publication in new window or tab >>Evaluation of the content of a web tool aimed to identify early markers related to fall risk among middle-aged people
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2020 (English)In: Health and Technology, ISSN 2190-7188, E-ISSN 2190-7196, p. 1571-1578Article in journal (Refereed) Published
Abstract [en]

Today, the health care sector has no test for early age-related deterioration in physical ability. The aim of this study was to evaluate questionnaires, videos and physical tests whose task will be to identify early markers related to an increased fall risk in middle-aged people. If the person is aware of deficits in physical ability related to fall risk, the person can then use that knowledge to perform relevant training that can strengthen the physical ability related to fall risk. Self-efficacy for balance and strength, physical ability related to fall risk and body composition were measured for 36 middle-aged test participants. This study shows that the tested physical exercises were useful for self-assessment of physical ability. Impairment in physical ability could not be identified solely with measurement of body composition, walking speed, questions, videos that show adjustments that are common in people with impaired balance, or an extended version of the Short version of Activities-specific Balance Confidence scale. This study indicates that a combination of questionnaires, videos and physical exercises can evaluate physical ability and act as a method to identify early markers related to increased fall risk. The questionnaire, videos and physical exercises can be implemented in a web tool that could make persons aware that they have decreased physical ability regarding fall risk or that they needlessly make physical compensations when performing daily activities and thus are missing opportunities to strengthen their physical ability every day.

National Category
Engineering and Technology Health Sciences
Identifiers
urn:nbn:se:mdh:diva-51698 (URN)10.1007/s12553-020-00482-x (DOI)000573475100001 ()2-s2.0-85091687628 (Scopus ID)
Projects
Embedded Sensor Systems for Health PlusSTARK - Skattning Tidigt av fysisk förmåga för Alla och Riktat stöd ger Kunskap och minskad fallrisk senare i livet
Available from: 2020-10-20 Created: 2020-10-20 Last updated: 2023-09-15Bibliographically approved
Hellstrom, P. A. & Folke, M. (2020). Novel Weight Estimation Analyses and the Development of the Wearable IngVaL System for Monitoring of Health Related Walk Parameters. International Journal On Advances in Life Sciences, 12(1/2), 16-23
Open this publication in new window or tab >>Novel Weight Estimation Analyses and the Development of the Wearable IngVaL System for Monitoring of Health Related Walk Parameters
2020 (English)In: International Journal On Advances in Life Sciences, ISSN 1942-2660, E-ISSN 1942-2660, Vol. 12, no 1/2, p. 16-23Article in journal (Refereed) Published
Abstract [en]

The total amount of lifted weights and lift frequency are moderate to strong risk factors for lower back pain. Measurement of carried weight is thereby of interest. The aim of this paper is to (1) present three novel analyses methods for estimation of weight during walk and (2) to describe the design process of the cost-effective research system IngVaL based on pedobarography. The paper will also (3) present the durability of the sensors. Motivations for choices in the system design are given for hardware, selection of sensor type, sensor implementation and calibration of sensors. To measure weight during walk with IngVaL, fifteen test persons made five walks each with a pseudo-random added extra weight. Three analyses methods were tested, for estimation of weight while walking, resulting in Root Mean Square Errors of 11.3 kg, 7.1 kg and 6.1 kg respectively. The durability of the sensors were tested in an outdoors walking condition. It can be concluded that the IngVaL system shows good durability and that weight during walk is possible to measure with simple analyses methods.

Place, publisher, year, edition, pages
United States: IARIA journals, 2020
Keywords
pedobarography, carried weight, portable, wearable, insole, in-shoe
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-50883 (URN)1942-2660 (ISBN)
Projects
ESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2020-09-29 Created: 2020-09-29 Last updated: 2020-11-11Bibliographically approved
Hellstrom, P. A. & Folke, M. (2019). Carried Weight Affects Walking Speed Monitoring with the IngVaL System. In: Bernd Blobel, Mauro Giacomini (Ed.), The Sixteenth International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2019, June 10-12, Studies in health technology and informatics, volume 261, pages 317-320, 2019: . Paper presented at The Sixteenth International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2019, June 10-12, Studies in health technology and informatics, volume 261, pages 317-320, 2019 (pp. 317-320). Nieuwe Hemweg 6B 1013 BG Amsterdam, The Netherlands: NLM (Medline), 261
Open this publication in new window or tab >>Carried Weight Affects Walking Speed Monitoring with the IngVaL System
2019 (English)In: The Sixteenth International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2019, June 10-12, Studies in health technology and informatics, volume 261, pages 317-320, 2019 / [ed] Bernd Blobel, Mauro Giacomini, Nieuwe Hemweg 6B 1013 BG Amsterdam, The Netherlands: NLM (Medline) , 2019, Vol. 261, p. 317-320Conference paper, Published paper (Refereed)
Abstract [en]

Earlier work has shown that the IngVaL pedobarography system can estimate walking speed during indoor walking using only three forefoot sensors. The aim of this study was to examine if monitoring of walking speed using data from these three forefoot sensors is affected of the weight a person carries, if the person performs a walk in a set speed on the treadmill. Shoe insoles with force sensing resistors were connected to an electronic unit for signal conditioning and sampling and then the data was sent via Bluetooth to a tablet. Fifteen test persons walked five times each carrying five different weights on the treadmill at 1 m/s. The force-time integrals for the sum of the three forefoot sensors were calculated. This study shows that the force-time integrals for the three forefoot sensors shows a linear relationship with the carried weight as long as the person is not fatigued.

Place, publisher, year, edition, pages
Nieuwe Hemweg 6B 1013 BG Amsterdam, The Netherlands: NLM (Medline), 2019
Series
Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365 ; 261
Keywords
carried weight, in-shoe, insole, pedobarography, portable, wearable
National Category
Medical Engineering
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-44661 (URN)10.3233/978-1-61499-975-1-317 (DOI)000624509800046 ()31156138 (PubMedID)2-s2.0-85067090455 (Scopus ID)978-1-61499-974-4 (ISBN)978-1-61499-975-1 (ISBN)
Conference
The Sixteenth International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2019, June 10-12, Studies in health technology and informatics, volume 261, pages 317-320, 2019
Funder
Knowledge Foundation, 20120275
Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2023-09-22Bibliographically approved
Hellstrom, P. A. & Folke, M. (2019). Monitoring of Carried Weight During Walk Using a Wearable Pedobarography System. In: Jaime Lloret, Universitat Politecnica de Valencia, Spain (Ed.), The Fifth International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, SPWID 2019, July 28-August 2, pages 5-8, 2019: . Paper presented at The Fifth International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, SPWID 2019, July 28-August 2, pages 5-8, 2019 (pp. 5-8). IARIA, PO Box 7827, Wilmington, DE 19803, USA: International Academy, Research and Industry Association (IARIA)
Open this publication in new window or tab >>Monitoring of Carried Weight During Walk Using a Wearable Pedobarography System
2019 (English)In: The Fifth International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, SPWID 2019, July 28-August 2, pages 5-8, 2019 / [ed] Jaime Lloret, Universitat Politecnica de Valencia, Spain, IARIA, PO Box 7827, Wilmington, DE 19803, USA: International Academy, Research and Industry Association (IARIA), 2019, p. 5-8Conference paper, Published paper (Refereed)
Abstract [en]

Personal health monitoring is advantageous in heavy work environments to reduce the risk of wear and tear and acute injuries. The study of forces between the plantar surface of the foot and a supporting structure, pedobarography, is a  promising candidate for monitoring carried weight during walk. The aim of this study was to evaluate the cost effective pedobarography measurement system, IngVaL. Two aspects are evaluated, namely, how well IngVaL can monitor carried weight during walk and if the novel implementation increased the durability. Fifteen test persons made five treadmill walks with a carried weight of 10, 20, 0, 15, and 5 kg. The equipoise analysis method was used. The Root Mean Square Error (RMSE) for estimation of the carried weight was 13.8 kg. A study with the earlier version of the measurement system had a RMSE of 23.3 kg. The earlier system, as well as commercial systems using this kind of sensors, have problems with sensor durability. The new sensor implementation, where the active sensor area boundary was no longer affected by mechanical stress, resulted in no broken sensors. This study shows an increased performance of carried weight estimation compared with earlier work, together with an improved sensor durability.

Place, publisher, year, edition, pages
IARIA, PO Box 7827, Wilmington, DE 19803, USA: International Academy, Research and Industry Association (IARIA), 2019
Series
SPWID, International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, ISSN 2519-8440 ; 5
Keywords
pedobarography; carried weight; portable; wearable; insole; in-shoe
National Category
Medical Engineering
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-45370 (URN)978-1-61208-734-4 (ISBN)
Conference
The Fifth International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, SPWID 2019, July 28-August 2, pages 5-8, 2019
Funder
Knowledge Foundation, 20120275
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2022-11-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8704-402X

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