| 1. |
- Åkerberg, Anna, 1974-
(author)
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An interactive health technology solution for encouraging physical activity : a first model based on a user perspective
- 2018
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Doctoral thesis (other academic/artistic)abstract
- Globally, the level of physical inactivity is increasing. The overall aim of this thesis was to develop and test a first model of an interactive health technology solution (called App&Move) that should encourage physically inactive adults to be more physically active. App&Move was iteratively developed based on the user perspective, a so-called user-centered design. First, available technology was assessed; the validity and reliability of one smartphone pedometer application and one commonly used traditional pedometer were investigated. It was found that none of the investigated pedometers could measure correctly in all investigated situations. However, measurements by a smartphone appli-cation was identified to have high potential when aimed at monitoring physical activity in everyday situations. As the next step, a questionnaire was developed and distributed in central Sweden. The 107 respondents who answered the questionnaire were divided and analyzed in groups of users and non-users of physical activity self-monitoring technology. The results showed that users and non-users of such technology mainly had similar opinions about desirable functions of the technology. To gain further knowledge concerning how to design App&Move, the target group physically inactive non-users participated in focus group interviews. Important results were that the technology should focus on encouragement rather than measurements and that it preferably should be integrated into already existing technology, if possible already owned and worn by the person. A brainstorming workshop confirmed that the smartphone was a suitable platform, and a decision to develop a smartphone application was taken. A first draft of App&Move was developed, focusing on encouragement and measuring everyday activity and exercise in minutes per day. App&Move was based on available physical activity recommendations and strategies for successful behavior change. App&Move was positively received in a user workshop and thereafter iteratively refined and developed based on further user input. App&Move was usability tested in 23 physically inactive adults who used App&Move for four weeks and answered two questionnaires. Three usability aspects, effectiveness, efficiency and satisfaction, were assessed as follows: acceptable, high and medium, and slight increases in activity minutes were observed during the test period. To conclude, this thesis has investigated the user perspective of physical activity self-monitoring technology with a target group of physically inactive adults. Based on these findings, a behavior change application for smartphone, App&Move, was presented. The usability test indicated promising results with respect to usability and indicated an ability to encourage the users to physical activity to some extent.
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| 2. |
- Abbaspour Asadollah, Sara, et al.
(author)
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Evaluation of surface EMG-based recognition algorithms for decoding hand movements
- 2019
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In: Medical and Biological Engineering and Computing. - : Springer. - 0140-0118 .- 1741-0444. ; 58:1, s. 83-100
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Journal article (peer-reviewed)abstract
- 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. [Figure not available: see fulltext.].
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| 3. |
- Abbaspour, Sara, 1984-
(author)
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Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition
- 2019
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Doctoral thesis (other academic/artistic)abstract
- Losing a limb causes difficulties in our daily life. To regain the ability to live an independent life, artificial limbs have been developed. Hand prostheses belong to a group of artificial limbs that can be controlled by the user through the activity of the remnant muscles above the amputation. Electromyogram (EMG) is one of the sources that can be used for control methods for hand prostheses. Surface EMGs are powerful, non-invasive tools that provide information about neuromuscular activity of the subjected muscle, which has been essential to its use as a source of control for prosthetic limbs. However, the complexity of this signal introduces a big challenge to its applications. EMG pattern recognition to decode different limb movements is an important advancement regarding the control of powered prostheses. It has the potential to enable the control of powered prostheses using the generated EMG by muscular contractions as an input. However, its use has yet to be transitioned into wide clinical use. Different algorithms have been developed in state of the art to decode different movements; however, the challenge still lies in different stages of a successful hand gesture recognition and improvements in these areas could potentially increase the functionality of powered prostheses. This thesis firstly focuses on improving the EMG signal’s quality by proposing novel and advanced filtering techniques. Four efficient approaches (adaptive neuro-fuzzy inference system-wavelet, artificial neural network-wavelet, adaptive subtraction and automated independent component analysis-wavelet) are proposed to improve the filtering process of surface EMG signals and effectively eliminate ECG interferences. Then, the offline performance of different EMG-based recognition algorithms for classifying different hand movements are evaluated with the aim of obtaining new myoelectric control configurations that improves the recognition stage. Afterwards, to gain proper insight on the implementation of myoelectric pattern recognition, a wide range of myoelectric pattern recognition algorithms are investigated in real time. The experimental result on 15 healthy volunteers suggests that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) outperform other classifiers. The real-time investigation illustrates that in addition to the LDA and MLE, multilayer perceptron also outperforms the other algorithms when compared using classification accuracy and completion rate.
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| 4. |
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| 5. |
- Afifi, S., et al.
(author)
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A Novel Medical Device for Early Detection of Melanoma
- 2019
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In: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 122-127
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Journal article (peer-reviewed)abstract
- 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.
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| 6. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(author)
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Multi-parameter Sensing Platform in ESS-H and E-care@home
- 2017
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In: 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.
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Conference paper (peer-reviewed)abstract
- 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.
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| 7. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(author)
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Run-Time Assurance for the E-care@home System
- 2018
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In: Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 225). - Cham : Springer International Publishing. - 9783319762128 - 9783319762135 ; , s. 107-110
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Conference paper (peer-reviewed)abstract
- 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. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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| 8. |
- Baig, M. M., et al.
(author)
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A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption
- 2017
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In: Journal of medical systems. - : Springer New York LLC. - 0148-5598 .- 1573-689X. ; 41:7
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Journal article (peer-reviewed)abstract
- 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.
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| 9. |
- Baig, Mirza Mansoor, et al.
(author)
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Clinical decision support systems in hospital care using ubiquitous devices : Current issues and challenges
- 2019
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In: Health Informatics Journal. - : SAGE PUBLICATIONS INC. - 1460-4582 .- 1741-2811. ; 25:3, s. 1091-1104
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Journal article (peer-reviewed)abstract
- 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.
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| 10. |
- Baig, M. M., et al.
(author)
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Machine learning-based clinical decision support system for early diagnosis from real-time physiological data
- 2016
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In: Proceedings/TENCON. - : Institute of Electrical and Electronics Engineers Inc.. - 9781509025961 ; , s. 2943-2946
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Conference paper (peer-reviewed)abstract
- 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.
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