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This paper presents a wearable inertial sensor network and its associated activity recognition algorithm for accurately recognizing human daily and sport activities. The proposed wearable inertial sensor network is composed of two wearable inertial sensing devices, which comprise a microcontroller, a triaxial accelerometer, a triaxial gyroscope, an RF wireless transmission module, and a power supply circuit. The activity recognition algorithm, consisting of procedures of motion signal acquisition, signal preprocessing, dynamic human motion detection, signal normalization, feature extraction, feature normalization, feature reduction, and activity recognition, has been developed to recognize human daily and sport activities by using accelerations and angular velocities. In order to reduce the computational complexity and improve the recognition rate simultaneously, we have utilized the nonparametric weighted feature extraction algorithm with the principal component analysis method for reducing the feature dimensions of inertial signals. All 23 participants wore the wearable sensor network on their wrist and ankle to execute 10 common domestic activities in human daily lives and 11 sport activities in a laboratory environment, and their activity recordings were collected to validate the effectiveness of the proposed wearable inertial sensor network and activity recognition algorithm. Experimental results showed that our approach could achieve recognition rates for the 10 common domestic activities of 98.23% and 11 sport activities of 99.55% by the 10-fold cross-validation strategy, which have successfully validated the effectiveness of the proposed wearable inertial sensor network and its activity recognition algorithm.

Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network