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      Unobtrusive Sensing for Home-Based Post-Stroke Rehabilitation

      , ,

      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      MEMS, unobtrusive, Radar, Thermal, Sensing, Post-stroke

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper proposes the fusion of low-cost unobtrusive heterogeneous sensors (MEMS thermal and radar sensors) to monitor the rehabilitation activities of post-stroke sufferers within home-based settings. Results of the proposed approach are planned to be compared with a standard EMG sensor to ascertain the authenticity, validity and repeatability of the newly introduced sensing solution.

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          Most cited references 10

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          Challenges, issues and trends in fall detection systems

          Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.
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            Recent advances in visual and infrared face recognition—a review

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              EMG signal decomposition: how can it be accomplished and used?

               Dan Stashuk (2001)
              Electromyographic (EMG) signals are composed of the superposition of the activity of individual motor units. Techniques exist for the decomposition of an EMG signal into its constituent components. Following is a review and explanation of the techniques that have been used to decompose EMG signals. Before describing the decomposition techniques, the fundamental composition of EMG signals is explained and after, potential sources of information from and various uses of decomposed EMG signals are described.
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                Author and article information

                Contributors
                Conference
                July 2018
                July 2018
                : 1-3
                Affiliations
                Ulster University

                School of Computing
                Ulster University

                NIBEC
                Article
                10.14236/ewic/HCI2018.300
                © Ekerete et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                HCI
                32
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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