Wearable magnetic induction-based approach toward 3D motion tracking

Negar Golestani,Mahta Moghaddam

doi:10.1038/s41598-021-98346-5

Abstract

Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy. This work presents a motion tracking system based on magnetic induction (MI) to tackle the challenges and limitations inherent in designing a wireless monitoring system. We integrated a realistic prototype of an MI sensor with machine learning techniques and investigated one-sensor and two-sensor configuration setups for motion reconstruction. This approach is successfully evaluated using measured and synthesized datasets generated by the analytical model of the MI system. The system has an average distance root-mean-squared error (RMSE) error of 3 cm compared to the ground-truth real-world measured data with Kinect.

Highlights

Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring
In20, we introduced magnetic induction-based human activity recognition (MIHAR), a wireless system based on magnetic induction combined with classification techniques to detect human activities
Due to the small radiation resistance of the coil, a negligible amount of energy propagates to the far-field. It removes the multipath fading effect resulting in a better quality of service (QoS) compared to conventional propagating wave systems[29]

Summary

Introduction

Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. There are many solutions for tracking body movement using different monitoring s ources[10] Computer visionbased methods, such as Kinect or optical motion capture (MoCap) systems, are the most commonly used techniques that allow users to interact with them and collect data on the user’s motion using depth sensors, color, and infrared cameras[11,12]. They inherit computer vision restrictions such as light dependency, coverage limitation, and high computational cost[13,14]. Inertial sensing can track limb movements by integrating over sensor measurements, though it is subject to drift since the estimation errors caused by the intrinsic noise can grow unbounded with time[20]

Methods

Results

Conclusion