Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living : a pilot study Li, Saiyi, Pathirana, Pubudu N. and Galea, Mary P. 2015, Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living : a pilot study, in EMBC 2015 : Biomedical Engineering : A Bridge to Improve the Quality of Health Care and the Quality of Life, IEEE, Piscataway, N.J., pp. 2701-2704, doi: 10.1109/EMBC.2015.7318949. Attached Files Name Description MIMEType Size Downloads li-motiontrajectory-postprint-2015.pdf Accepted version application/pdf 447.48KB 9 Title Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living : a pilot study Author(s) Li, Saiyi Pathirana, Pubudu N. orcid.org/0000-0001-8014-7798 Galea, Mary P. Conference name IEEE Engineering in Medicine and Biology Society. Conference (37th : 2015 : Milan, Italy) Conference location Milan, Italy Conference dates 25-29 Aug. 2015 Title of proceedings EMBC 2015 : Biomedical Engineering : A Bridge to Improve the Quality of Health Care and the Quality of Life Editor(s) Patton, J. Publication date 2015 Start page 2701 End page 2704 Total pages 4 Publisher IEEE Place of publication Piscataway, N.J. Summary Since 1998, tele-rehabilitation has been extensively studied for its potential capacity of saving time and cost for both therapists and patients. However, one gap hindering the deployment of tele-rehabilitation service is the approach to evaluate the outcome after tele-rehabilitation exercises without the presence of professional clinicians. In this paper, we propose an approach to model jerky and jerky-free movement trajectories with hidden Markov models (HMMs). The HMMs are then utilised to identify the jerky characteristics in a motion trajectory, thereby providing the number and amplitude of jerky movements in the specific length of the trajectory. Eventually, the ability of performing functional upper extremity tasks can be evaluated by classifying the motion trajectory into one of the pre-defined ability levels by looking at the number and amplitude of jerky movements. The simulation experiment confirmed that the proposed method is able to correctly classify motion trajectories into various ability levels to a high degree. ISBN 9781424492718 ISSN 1557-170X Language eng DOI 10.1109/EMBC.2015.7318949 Field of Research 010203 Calculus of Variations, Systems Theory and Control Theory 010204 Dynamical Systems in Applications 090602 Control Systems, Robotics and Automation Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2015, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30082523 Document type: Conference Paper Collections: School of Engineering Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 01 Apr 2016, 15:40:53 EST Fri, 01 Apr 2016, 15:49:05 EST Fri, 01 Apr 2016, 16:06:45 EST Sat, 02 Apr 2016, 04:00:37 EST Sat, 02 Apr 2016, 05:09:25 EST Thu, 28 Jul 2016, 09:56:48 EST Thu, 28 Jul 2016, 10:01:01 EST Wed, 12 Oct 2016, 15:03:06 EST Wed, 01 Mar 2017, 07:53:51 EST Mon, 15 Jan 2018, 13:14:09 EST Fri, 07 Sep 2018, 17:55:45 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 183 Abstract Views, 11 File Downloads  -  Detailed Statistics Created: Fri, 01 Apr 2016, 16:06:45 EST

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