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      A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

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          Abstract

          Rapid growth of the aged population has caused an immense increase in the demand for healthcare services. Generally, the elderly are more prone to health problems compared to other age groups. With effective monitoring and alarm systems, the adverse effects of unpredictable events such as sudden illnesses, falls, and so on can be ameliorated to some extent. Recently, advances in wearable and sensor technologies have improved the prospects of these service systems for assisting elderly people. In this article, we review state-of-the-art wearable technologies that can be used for elderly care. These technologies are categorized into three types: indoor positioning, activity recognition and real time vital sign monitoring. Positioning is the process of accurate localization and is particularly important for elderly people so that they can be found in a timely manner. Activity recognition not only helps ensure that sudden events (e.g., falls) will raise alarms but also functions as a feasible way to guide people’s activities so that they avoid dangerous behaviors. Since most elderly people suffer from age-related problems, some vital signs that can be monitored comfortably and continuously via existing techniques are also summarized. Finally, we discussed a series of considerations and future trends with regard to the construction of “smart clothing” system.

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          A Survey on Human Activity Recognition using Wearable Sensors

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            A survey of indoor positioning systems for wireless personal networks

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              Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

              The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                10 February 2017
                February 2017
                : 17
                : 2
                : 341
                Affiliations
                [1 ]Institute of Applied Micro-Nano Science and Technology, Chongqing Technology and Business University, Chongqing 400067, China; Wang1072020701@ 123456hotmail.com
                [2 ]College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, China
                [3 ]Institute for Microsystems (IMS), Faculty of Technology and Maritime Science, University College of Southeast Norway (HSN), Horten 3184, Norway
                Author notes
                [* ]Correspondence: zhaochu.yang@ 123456ctbu.edu.cn (Z.Y.); tao.dong@ 123456usn.no (T.D.); Tel.: +86-23-6276-8805 (Z.Y.); +47-3100-9321 (T.D.)
                [†]

                These authors contributed equally to this work.

                Article
                sensors-17-00341
                10.3390/s17020341
                5336038
                28208620
                85cd3a73-d1ce-412f-9f12-104215329921
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 November 2016
                : 24 January 2017
                Categories
                Review

                Biomedical engineering
                elderly care,wearable technologies,indoor positioning,human activity recognition,vital sign monitoring

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