Accurate human activity recognition with multi-task learning

Abstract

Human activities recognition (HAR) in wearable devices is a promising technology in pervasive computing. However, the traditional method often regards human activity recognition as a single label recognition problem, ignoring the association between the current activity mode, personal motion mode and sensor wearing position. This paper proposes a multi-task human activity recognition multi-task learning framework based on supervised learning, which not only considers the activity, but also considers the identity of the wearer, gender and the position of the sensor on the body. We extracted the time-domain and frequency-domain features of the original data, and classified the data through a multi-task learning framework composed of a fully connected network and a convolutional neural network. We employ a public data set composed of 15 experimenters, 8 movements and 7 body positions. Only 30 $$\%$$ of the data is used to train the model, which can achieve high precision. The experimental results show that the classification accuracy of activity recognition can reach 90.8 $$\%$$ , body position recognition can reach 98.7 $$\%$$ , wearer identity recognition can reach 97.5 $$\%$$ , gender recognition can reach 98.7 $$\%$$ . We call the model trained with 30 $$\%$$ data as a pre-trained model, and then put personal data into the pre-trained model for fine-tune. Using a pre-trained model for fine-tune on personal data can achieve up to 95.6 $$\%$$ activity recognition accuracy.