Fall detection system based on inertial mems sensors: Analysis design and realization

This paper presents the development and analysis of inertial MEMS sensor based system that can detect falls in real time. The system is a major part of mobile human airbag system which prevents the elderly from fall induced fractures. The fall detection system hardware was designed, which could monitor the motions of the feet and waist and detect the falls in real time. Micro Inertial Measurement Units (μ IMUs) was applied in this system with Zigbee network and the fall detection algorithm what was constituted of three sub algorithms also was developed. The system was designed based on data analysis, in order to select the optimal parts for monitoring human motion and verify the algorithm performance, performance for different parts was compared by employing the pattern recognition based sub-algorithm and performance for different combination of human body segments and joints was also compared to get the better result. A wearable motion capture device was utilized to acquire the motion data. The effective extracting features were carried out and the motion classification performance was achieved and compared using the J48 decision tree classifier. Experimental results showed that the waist is the best location for motion monitoring with detection Sensitivity of 95.5%, the Specificity of 98.8% and the overall accuracy of 97.792%. Furthermore, the combination of the waist and feet sensing data was adopted with the Sensitivity of 98.9%, the Specificity of 98.5% and the overall accuracy of 98.565%. Based on the analysis, the system was designed to monitoring the motion of the combination, and the pattern recognition based sub-algorithm was also verified.