Placement Effect of Motion Sensors for Human Activity Recognition using LSTM Network

There are various possibilities for wearable sensors to determine the recognition of human condition. In order to theoretically drive with wearable sensor technology, several difficult issues have been studied and solved. We currently succeeded in computing the efficient high performance deep learning model for the sensor-based human activity recognition (HAR). There is, however, an influence study challenge regarding the impact of the body-mounted sensor position on the Long Short-Term Memory (LSTM) network. In this work, we propose to conduct several experiments in order to discover the optimal placement for each physical activity. The experimental results show that by using heterogeneous sensor data fusion, the chest position is ideal for physical activity identification with a maximum accuracy of 94.18%.