BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors

Abstract

Sensor-based human activity recognition has been widely used in various fields; however, there are still challenges involving recognition of daily complex human activities using sensors. In order to solve the problem of timeliness and homogeneity of recognition functions in human activity recognition models, we propose a human activity recognition model called ’BSTCA-HAR’ based on a long short-term memory (LSTM) network. The approach proposed in this paper combines an attention mechanism and a temporal convolutional network (TCN). The learning and prediction units in the model can efficiently learn important action data while capturing long time-dependent information as well as features at different time scales. Our series of experiments on three public datasets (WISDM, UCI-HAR, and ISLD) with different data features confirm the feasibility of the proposed method. This method excels in dynamically capturing action features while maintaining a low number of parameters and achieving a remarkable average accuracy of 93%, proving that the model has good recognition performance.

Keywords

• humanactivity recognition
• sensor data
• LSTM
• deep learning