Investigating the impact of sensor axis combinations on activity recognition and fall detection: an empirical study

Abstract

Activity recognition is a fundamental concept widely embraced within the realm of healthcare. Leveraging sensor fusion techniques, particularly involving accelerometers (A), gyroscopes (G), and magnetometers (M), this technology has undergone extensive development to effectively distinguish between various activity types, improve tracking systems, and attain high classification accuracy. This research is dedicated to augmenting the effectiveness of activity recognition by investigating diverse sensor axis combinations while underscoring the advantages of this approach. In pursuit of this objective, we gathered data from two distinct sources: 20 instances of falls and 16 daily life activities, recorded through the utilization of the Motion Tracker Wireless (MTw), a commercial product. In this particular experiment, we meticulously assembled a comprehensive dataset comprising 2520 tests, leveraging the voluntary participation of 14 individuals (comprising 7 females and 7 males). Additionally, data pertaining to 7 cases of falls and 8 daily life activities were captured using a cost-effective, environment-independent Activity Tracking Device (ATD). This alternative dataset encompassed a total of 1350 tests, with the participation of 30 volunteers, equally divided between 15 females and 15 males. Within the framework of this research, we conducted meticulous comparative analyses utilizing the complete dataset, which encompassed 3870 tests in total. The findings obtained from these analyses convincingly establish the efficacy of recognizing both fall incidents and routine daily activities. This investigation underscores the potential of leveraging affordable IoT technologies to enhance the quality of everyday life and their practical utility in real-world scenarios.