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Sensors 2013, 13(9), 11289-11313; doi:10.3390/s130911289

Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

School of Computer Science & Engineering, Hebrew University of Jerusalem, Jerusalem 91904, Israel
These authors contributed equally to this work.
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Received: 3 July 2013 / Revised: 5 August 2013 / Accepted: 10 August 2013 / Published: 23 August 2013
(This article belongs to the Special Issue Wearable Gait Sensors)
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Abstract

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. View Full-Text
Keywords: Body Area Network; gait analysis; daily activity; Kalman filter; RSSI Body Area Network; gait analysis; daily activity; Kalman filter; RSSI
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Blumrosen, G.; Luttwak, A. Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements. Sensors 2013, 13, 11289-11313.

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