Algorithm of the Fall Prediction Based on the Double Foot Pressure and Micro Inertial Sensors

This paper contains the development and analysis of the human motion state and the algorithm of the fall prediction based on the double foot pressure and the micro inertial MEMS sensors. The fall prediction hardware system consists of three parts, the double foot nodes and the waist node and how it was designed, which could measure the foot pressure parameters and the inertial parameters in different human motion states and could detect the falls in real time. What's more, the foot pressure measurement units and the Micro Inertial Measurement Units (IMUs) were applied in this system with wired network and the fall prediction algorithm was constituted of large numbers of threshold judgments which can detect different falls directly. With this hardware system, the foot pressure data and the motion data can be captured in real time. Then, these data will be dealt with through J48 decision tree classifier. Experiment results showed that the lead time (the time ahead of collision) of fall can be improved to 180ms and different falls can be recognized with different logic trees which were judged through the foot pressure threshold, the angular velocity threshold and the acceleration threshold. Based on the analysis, it can be showed that in the recognition of fall and ADL(Activities of Daily Life), the Sensitivity, the Specificity and the overall accuracy were all over 96%. While in the recognition of different falls, they can all achieve over 92%.