Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting

Shaopeng Liu,Lingfei Mo,Robert X Gao,Lujie Zeng

doi:10.3390/info10060197

Abstract

This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performance on some specific activity classes. Through class-specific weighted majority voting, the recognition accuracy of 10 PA types has been improved from 86% to 92% compared with the non-combination approach. Furthermore, the combination method has shown to be effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition and has better performance in monitoring physical activities of varying intensities than traditional homogeneous classifiers.

Highlights

Physical activity (PA) is any bodily movement worked by skeletal muscles that requires more energy expenditure than resting [1], such as walking, running, swimming, or aerobic exercise and strength training
As for each individual, the actual PA labels and time period executed by the subjects were recorded, and the dataflow with the different PA types was put into storage correspondingly by the wireless integrated measurement system (WIMS)
Every PA type would be performed for 7 min, and a 2-min rest period was given to calm heart rate

Summary

Introduction

Physical activity (PA) is any bodily movement worked by skeletal muscles that requires more energy expenditure than resting [1], such as walking, running, swimming, or aerobic exercise and strength training. Getting proper physical activity throughout the day can lower the risk of type. Most people do not do enough physical activity, because it is not easy to be measured. Accurate tracking and monitoring of PA under free-living conditions is of significant importance to cultivate scientific living habits and improve an individual’s health. The target of PA monitoring is oriented to recognize the type of activity, duration of time, and the intensity of daily activities in real-time. By estimating the energy consumption, it provides important guidance for people’s scientific fitness. Hendelman et al have demonstrated the correlations between accelerometer counts and energy expenditure [2]

Methods

Results

Conclusion