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Sensors 2016, 16(11), 1965; doi:10.3390/s16111965

A Real-Time Kinect Signature-Based Patient Home Monitoring System

1
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
2
Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan 52621, Israel
3
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
4
Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
Current affiliation: Multiscale Systems Biology and Modeling Group, IBM T.J. Watson Research Center, Yorktown Heights 10598, NY, USA.
*
Author to whom correspondence should be addressed.
Academic Editors: Octavian Adrian Postolache, Alex Casson and Subhas Mukhopadhyay
Received: 9 August 2016 / Revised: 10 November 2016 / Accepted: 12 November 2016 / Published: 23 November 2016
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
View Full-Text   |   Download PDF [5341 KB, uploaded 23 November 2016]   |  

Abstract

Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect (“Kinect”) system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints’ estimations, and erroneous multiplexing of different subjects’ estimations to one. This study addresses these limitations by incorporating a set of features that create a unique “Kinect Signature”. The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities. View Full-Text
Keywords: Kinect; motion tracking; gait analysis; artifact detection Kinect; motion tracking; gait analysis; artifact detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Blumrosen, G.; Miron, Y.; Intrator, N.; Plotnik, M. A Real-Time Kinect Signature-Based Patient Home Monitoring System. Sensors 2016, 16, 1965.

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