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dc.contributor.authorQi, Junen
dc.contributor.authorYang, Poen
dc.contributor.authorWaraich, Atifen
dc.contributor.authorDeng, Zhikunen
dc.contributor.authorZhao, Youbingen
dc.contributor.authorYang, Yunen
dc.date.accessioned2020-01-31T13:07:05Z
dc.date.available2020-01-31T13:07:05Z
dc.date.issued2018-09-26
dc.identifier.citationQi J, Yang P, Waraich A, Deng Z, Zhao Y, Yang Y (2018) 'Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: a systematic review', Journal of Biomedical Informatics, 87, pp.138-153.en
dc.identifier.issn1532-0464
dc.identifier.pmid30267895
dc.identifier.doi10.1016/j.jbi.2018.09.002
dc.identifier.urihttp://hdl.handle.net/10547/623802
dc.description.abstractDue to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S153204641830176Xen
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectInternet of thingsen
dc.subjectphysical activity recognitionen
dc.subjectphysical activity monitoringen
dc.subjectsensor-baseden
dc.subjectsystematic reviewen
dc.subjectB800 Medical Technologyen
dc.titleExamining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: a systematic reviewen
dc.typeArticleen
dc.identifier.eissn1532-0480
dc.contributor.departmentYunnan Universityen
dc.contributor.departmentLiverpool John Moore Universityen
dc.contributor.departmentUniversity of Bedfordshireen
dc.identifier.journalJournal of Biomedical Informaticsen
dc.date.updated2020-01-31T13:03:26Z
dc.description.notefull text from White Rose repository
refterms.dateFOA2020-04-23T08:41:00Z
html.description.abstractDue to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.


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