title: A Real-Time Kinect Signature-Based Patient Home Monitoring System.
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.
authors.affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. gaddi.b@gmail.com.
authors.forename: Gaddi
authors.initials: G
authors.lastname: Blumrosen
authors.affiliation: Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan 52621, Israel. yael_miron@yahoo.com.
authors.forename: Yael
authors.initials: Y
authors.lastname: Miron
authors.affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. nathan@intrators.com.|Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel. nathan@intrators.com.
authors.forename: Nathan
authors.initials: N
authors.lastname: Intrator
authors.affiliation: Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan 52621, Israel. Meir.PlotnikPeleg@sheba.health.gov.il.|Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel. Meir.PlotnikPeleg@sheba.health.gov.il.|Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel. Meir.PlotnikPeleg@sheba.health.gov.il.
authors.forename: Meir
authors.initials: M
authors.lastname: Plotnik
mesh_terms: D001696:Biomechanical Phenomena; D005684:Gait / Q000502:physiology; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D012984:Software; D016138:Walking / Q000502:physiology
keywords: Kinect; artifact detection; gait analysis; motion tracking