OBJECTIVE: In this letter, we propose a novel method for classifying daily wrist activities by using a smart band. APPROACH: Triaxial acceleration data are collected by built-in sensors of the smart band during experiments regarding five activities, i.e. texting, calling, placing a hand in a pocket, carrying a suitcase, and swinging a hand. We analyze patterns in the sensor signals during these activities based on three types of features, i.e. norm, norm-variance, and frequency-domain features. After extracting the significant features, a multi-class support vector machine algorithm is applied to classify these activities. MAIN RESULTS: We obtained recognition error rates of approximately 2.7% by applying the proposed method to the experimental dataset.
OBJECTIVE: In this letter, we propose a novel method for classifying daily wrist activities by using a smart band. APPROACH: Triaxial acceleration data are collected by built-in sensors of the smart band during experiments regarding five activities, i.e. texting, calling, placing a hand in a pocket, carrying a suitcase, and swinging a hand. We analyze patterns in the sensor signals during these activities based on three types of features, i.e. norm, norm-variance, and frequency-domain features. After extracting the significant features, a multi-class support vector machine algorithm is applied to classify these activities. MAIN RESULTS: We obtained recognition error rates of approximately 2.7% by applying the proposed method to the experimental dataset.