A Single Smart Ring for Monitoring 20 Kinds of Multi‐Intensity Daily Activities––From Kitchen Work to Fierce Exercises

Abstract

Statistical analysis of human daily activities contributes to time planning and health management. It also helps people make healthcare judgments and disease predictions when combined with big data technology. However, traditional methods for recognition of human daily activities based on vision and multisensors have limitations due to poor portability and weak capability in multiple activities detection. Herein, the development of a wearable human activity recognition (HAR) smart ring capable of recognizing at least 20 multi‐intensity activities, i.e., motions ranging from clean‐the‐desk to playing basketball, based on a microinertial measurement unit and a hierarchical decision algorithm is proposed. Users only need to wear the smart ring on the index finger of the right hand to accurately identify 20 common activities that are classified as light, vigorous, and fierce. A novel hierarchical decision algorithm using 70 features is proposed to improve the accuracy and speed of recognizing common human activities and provides a final recognition accuracy of 98.10% for 20 types of activities. This extremely portable, reliable, and high‐accuracy HAR solution is a significant advancement in providing real‐time quantitative data for personal lifestyle supervision, time planning, and healthcare management.

Keywords

• hierarchical decision
• human activity recognition (HAR)
• machine learning
• smart ring