LAUSR

Human activity recognition (HAR) has received a lot of attention due to its wide applications in recent years, while the improvement of recognition accuracy is seemingly considered to be one of the great challenges in this field. In this paper, a novel wearable device for improving the activity recognition accuracy is proposed based on the different multiple sensors, which simultaneously collects the muscle activity and motion information. The muscular activity is monitored by measuring the air pressure in an air bladder contacting the targeted muscle, while the motion information, such as three-axis accelerations and angular velocities of body movement, is collected via the on-body inertial measurement unit (IMU) sensor. The performance of the air-pressure sensor is verified by comparing with the electromyography and the IMU sensors. To implement our device, we collect the labeled activities data from eight subjects as they perform 11 daily activities. Some commonly used features from raw data are calculated, and five popular classification techniques are evaluated in terms of the accuracy, recall, precision, and F-measure. The experimental results indicate that the proposed wearable device can improve the performance of HAR system. Particularly, the usage of air-pressure sensor can eliminate the confusions among dynamic activities, such as walking and going upstairs, which is an open problem in HAR.