| 1. |
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| 2. |
- Kehoe, Laura, et al.
(författare)
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Make EU trade with Brazil sustainable
- 2019
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 3. |
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| 4. |
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| 5. |
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| 6. |
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| 7. |
- Åkerberg, Anna, 1974-
(författare)
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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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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 8. |
- Abbaspour Asadollah, Sara, et al.
(författare)
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Evaluation of surface EMG-based recognition algorithms for decoding hand movements
- 2019
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Ingår i: Medical and Biological Engineering and Computing. - : Springer. - 0140-0118 .- 1741-0444. ; 58:1, s. 83-100
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Tidskriftsartikel (refereegranskat)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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| 9. |
- Abbaspour, Sara, et al.
(författare)
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A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet
- 2016
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Ingår i: Journal of Electromyography & Kinesiology. - : Elsevier BV. - 1050-6411 .- 1873-5711. ; 26, s. 52-59
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Tidskriftsartikel (refereegranskat)abstract
- 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).
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| 10. |
- Abbaspour, Sara, 1984-, et al.
(författare)
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ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA
- 2015
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Ingår i: Studies in Health Technology and Informatics, Volume 211. - 9781614995159 ; , s. 91-97
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 11. |
- Abbaspour, Sara, 1984-
(författare)
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Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 12. |
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| 13. |
- Abbaspour, Sara, et al.
(författare)
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Evaluation of wavelet based methods in removing motion artifact from ECG signal
- 2015
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Ingår i: IFMBE Proceedings. - Cham : Springer International Publishing. - 9783319129662 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 14. |
- Abbaspour, Sara, 1984-
(författare)
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Proposing Combined Approaches to Remove ECG Artifacts from Surface EMG Signals
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Electromyography (EMG) is a tool routinely used for a variety of applications in a very large breadth of disciplines. However, this signal is inevitably contaminated by various artifacts originated from different sources. Electrical activity of heart muscles, electrocardiogram (ECG), is one of sources which affects the EMG signals due to the proximity of the collection sites to the heart and makes its analysis non-reliable. Different methods have been proposed to remove ECG artifacts from surface EMG signals; however, in spite of numerous attempts to eliminate or reduce this artifact, the problem of accurate and effective de-noising of EMG still remains a challenge. In this study common methods such as high pass filter (HPF), gating method, spike clipping, hybrid technique, template subtraction, independent component analysis (ICA), wavelet transform, wavelet-ICA, artificial neural network (ANN), and adaptive noise canceller (ANC) and adaptive neuro-fuzzy inference system (ANFIS) are used to remove ECG artifacts from surface EMG signals and their accuracy and effectiveness is investigated. HPF, gating method and spike clipping are fast; however they remove useful information from EMG signals. Hybrid technique and ANC are time consuming. Template subtraction requires predetermined QRS pattern. Using wavelet transform some artifacts remain in the original signal and part of the desired signal is removed. ICA requires multi-channel signals. Wavelet-ICA approach does not require multi-channel signals; however, it is user-dependent. ANN and ANFIS have good performance, but it is possible to improve their results by combining them with other techniques. For some applications of EMG signals such as rehabilitation, motion control and motion prediction, the quality of EMG signals is very important. Furthermore, the artifact removal methods need to be online and automatic. Hence, efficient methods such as ANN-wavelet, adaptive subtraction and automated wavelet-ICA are proposed to effectively eliminate ECG artifacts from surface EMG signals. To compare the results of the investigated methods and the proposed methods in this study, clean EMG signals from biceps and deltoid muscles and ECG artifacts from pectoralis major muscle are recorded from five healthy subjects to create 10 channels of contaminated EMG signals by adding the recorded ECG artifacts to the clean EMG signals. The artifact removal methods are also applied to the 10 channels of real contaminated EMG signals from pectoralis major muscle of the left side. Evaluation criteria such as signal to noise ratio, relative error, correlation coefficient, elapsed time and power spectrum density are used to evaluate the performance of the proposed methods. It is found that the performance of the proposed ANN-wavelet method is superior to the other methods with a signal to noise ratio, relative error and correlation coefficient of 15.53, 0.01 and 0.98 respectively.
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| 15. |
- Afifi, S., et al.
(författare)
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A Novel Medical Device for Early Detection of Melanoma
- 2019
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Ingår i: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 122-127
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Tidskriftsartikel (refereegranskat)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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| 16. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Generic System-level Framework for Self-Serve Health Monitoring System through Internet of Things(IoT)
- 2015
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Ingår i: Studies in Health Technology and Informatics, Volume 211. - 9781614995159 ; , s. 305-307
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 17. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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An Overview on the Internet of Things for Health Monitoring Systems
- 2016
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Ingår i: 2nd EAI International Conference on IoT Technologies for HealthCare HealthyIoT2015. - Cham : Springer International Publishing. ; , s. 429-436
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 18. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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Healthcare Service at Home : An Intelligent Health Monitoring System for Elderly
- 2015
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Ingår i: Medicinteknikdagarna 2015 MFT 2015.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 19. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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Multi-parameter Sensing Platform in ESS-H and E-care@home
- 2017
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Ingår 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.
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Konferensbidrag (refereegranskat)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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| 20. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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Run-Time Assurance for the E-care@home System
- 2018
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Ingår i: 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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Konferensbidrag (refereegranskat)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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| 21. |
- Baig, M. M., et al.
(författare)
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A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption
- 2017
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Ingår i: Journal of medical systems. - : Springer New York LLC. - 0148-5598 .- 1573-689X. ; 41:7
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Tidskriftsartikel (refereegranskat)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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| 22. |
- Baig, M. M., et al.
(författare)
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Advanced decision support system for older adults
- 2015
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Ingår i: Studies in Health Technology and Informatics, vol. 211. - 9781614995159 ; , s. 235-240
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 23. |
- Baig, Mirza Mansoor, et al.
(författare)
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Clinical decision support systems in hospital care using ubiquitous devices : Current issues and challenges
- 2019
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Ingår i: Health Informatics Journal. - : SAGE PUBLICATIONS INC. - 1460-4582 .- 1741-2811. ; 25:3, s. 1091-1104
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Tidskriftsartikel (refereegranskat)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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| 24. |
- Baig, M. M., et al.
(författare)
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Machine learning-based clinical decision support system for early diagnosis from real-time physiological data
- 2016
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Ingår i: Proceedings/TENCON. - : Institute of Electrical and Electronics Engineers Inc.. - 9781509025961 ; , s. 2943-2946
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Konferensbidrag (refereegranskat)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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| 25. |
- Baig, M.M., et al.
(författare)
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Tablet-based Patient Monitoring and Decision Support Systems in Hospital Care
- 2015
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Ingår i: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). - 9781424492701 ; , s. 1215-1218
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Konferensbidrag (refereegranskat)abstract
- 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.
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