Triangular membership function based real-time gesture monitoring system for physical disorder detection

Abstract

A novel approach to distinguish 25 body gestures enlightening physical disorders in young and elder individuals is explained using the proposed system. Here a well-known human sensing device, Kinect sensor is used which approximates the human body by virtue of 20 body joints and produces a data stream from which skeleton of the human body is traced. Sampling rate of the data stream is 30 frames per second where every frame represents a body gesture. The overall system is bifurcated into two parts. The offline part calculates 19 features from each frame representing a diseased gesture. These features are angle and distance information between 20 body joints. Features correspond to a definite pattern for a specific body gesture. In online part, triangular fuzzy matching based algorithm performs to detect real-time gestures with 90.57% accuracy. For achieving better accuracy, decision tree is enforced to separate sitting and standing body gestures. The proposed approach is observed to outperform several contemporary approaches in terms of accuracy while presenting a simple system which is based on medical knowledge and is capable of distinguishing as large as 25 gestures.