Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion.

Jack H Geissinger,Alan T Asbeck

doi:10.3390/s20216330

Abstract

In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics estimates using a small number of sensors. To accomplish this, we introduced a new dataset (the Virginia Tech Natural Motion Dataset) of full-body human motion capture using XSens MVN Link that contains more than 40 h of unscripted daily life motion in the open world. Using this dataset, we conducted self-supervised machine learning to do kinematics inference: we predicted the complete kinematics of the upper body or full body using a reduced set of sensors (3 or 4 for the upper body, 5 or 6 for the full body). We used several sequence-to-sequence (Seq2Seq) and Transformer models for motion inference. We compared the results using four different machine learning models and four different configurations of sensor placements. Our models produced mean angular errors of 10–15 degrees for both the upper body and full body, as well as worst-case errors of less than 30 degrees. The dataset and our machine learning code are freely available.

Highlights

Estimates of human motion for monitoring and analysis are useful in many circumstances.In sports and fitness applications, quantitative data on a person’s kinematics can aid in their training more effectively
We used the XSens MVN Link because inertial motion capture is well-suited to capturing natural motion, and this system is wireless with an on-body data logger
Note that we study upper-body motion inference, and we study Transformers as another deep learning algorithm

Summary

Introduction

Estimates of human motion for monitoring and analysis are useful in many circumstances. In sports and fitness applications, quantitative data on a person’s kinematics can aid in their training more effectively. Monitoring workers’ postures may help them avoid injuries. Rehabilitation may benefit from quantitative data on a patient’s capabilities, enabling therapists to customize training programs and observe progress. Exoskeletons and prosthetics can benefit from a quantitative understanding of human motion, since they must work cooperatively with the body. Each of these applications is becoming more prevalent, resulting in a need for databases of accurate human motion, and methods for using sensors to estimate human kinematics

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