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Updated: Feb 27, 2026
Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
Daily wrist activity classification using a smart band
Nhan Duc Nguyen1, Phuc Huu Truong1, Gu-Min Jeong1
1School of Electrical Engineering, Kookmin University, Jeongneung-dong, Seongbukgu, 02707 Korea.
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.
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