Limbs and Muscle Movement Detection using Gait Analysis

Human Gait is a biometric that is used for medical diagnostics having unobtrusive learning methods. If Gait is compared to other biometrics recognition methods, gait recognition technique possesses some special impacts for detecting defects on muscles of athletes and also to determine the mode of movement of the limbs. Comparing with presently available methods of biometric recognition, human Gait recognition is not free from limitations. Some incapability are found at the time of Gait recognition likely for changed angle of view, speed in human walking or when a load is being carried-out. The work presented here compares Gait recognition rate respect to template extracted feature what requires processing of every frame in a complete Gait cycle. The proposed methodology had used Gait Energy Image (GEI) as feature that improves the detection rate than some other methodologies. Our proposed methodology implements the GEI feature that helps to detect the defects in the muscles and suggests treatment for the athlete. In this paper we had obtained nearly 58% of recognition accuracy. Compared to some other methods the rate is quite better. Implementation of such system in real time application will help to detect problem in muscles of athletes and also to select wearable shoes and sensors for them. This will significantly change the diagnostic process of muscles and limbs diseases. Gait analysis is also very important in identification and verification of unauthorized entry in any area under surveillance.