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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.
Gaddi Blumrosen; Yael Miron; Nathan Intrator; Meir Plotnik. A Real-Time Kinect Signature-Based Patient Home Monitoring System. Sensors 2016, 16, 1965 .
AMA StyleGaddi Blumrosen, Yael Miron, Nathan Intrator, Meir Plotnik. A Real-Time Kinect Signature-Based Patient Home Monitoring System. Sensors. 2016; 16 (11):1965.
Chicago/Turabian StyleGaddi Blumrosen; Yael Miron; Nathan Intrator; Meir Plotnik. 2016. "A Real-Time Kinect Signature-Based Patient Home Monitoring System." Sensors 16, no. 11: 1965.
Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts’ displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.
Gaddi Blumrosen; Ami Luttwak. Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements. Sensors 2013, 13, 11289 -11313.
AMA StyleGaddi Blumrosen, Ami Luttwak. Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements. Sensors. 2013; 13 (9):11289-11313.
Chicago/Turabian StyleGaddi Blumrosen; Ami Luttwak. 2013. "Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements." Sensors 13, no. 9: 11289-11313.