Toward Hand-Dominated Activity Recognition Systems with Wristband-Interaction Behavior Analysis

The increasing usage of wearable devices for ambulatory monitoring and pervasive computing systems has given rise to the need of convenient and efficient activity recognition techniques. Hand-dominated activity recognition has great potential in understanding users' gesture and providing context-aware computing services. This paper investigates the feasibility and applicability on the usage of wristband-interaction behavior for recognizing hand-dominated activities, with the advantage of great compliance and long wearing time. For each action, sensor data from wristband are analyzed to obtain kinematic sequences. The sequences are then depicted by statistics-, frequency-, and wavelet-domain features for providing accurate and fine-grained characterization of hand-dominated actions, and the correlation between the wristband-sensor features and the actions is analyzed. Classification techniques (Naive Bayes, nearest neighbor, neural network, support vector machine, and Random Forest) are applied to the feature space for performing hand-dominated activity recognition. Analyses are conducted using the data from 51 participants with a diversity in gender, age, weight, and height. Extensive experiments demonstrate the efficacy of the proposed approach, achieving a recognition rate of 97.29% and an {F} -score above 0.94. Additional experiments on the effect of feature selection and wristband sampling rate are provided to further examine the effectiveness of our approach. Our data are publicly available.