Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning

TL;DR

This paper introduces MT-AIM, a deep learning model that uses synthetic data generation and feature augmentation to accurately classify lower back movements using low-cost, fabric-based wearable sensors, facilitating remote assessment.

Contribution

The study presents a novel deep learning pipeline that effectively handles small, noisy datasets from fabric-based sensors by generating synthetic data and predicting joint kinematics.

Findings

Achieved state-of-the-art accuracy in classifying back movements.

Demonstrated effectiveness of synthetic data augmentation in sensor-based movement analysis.

Bridged the gap between wearable sensor data and movement classification accuracy.

Abstract

Back pain is a pervasive issue affecting a significant portion of the population, often worsened by certain movements of the lower back. Assessing these movements is important for helping clinicians prescribe appropriate physical therapy. However, it can be difficult to monitor patients' movements remotely outside the clinic. High-fidelity data from motion capture sensors can be used to classify different movements, but these sensors are costly and impractical for use in free-living environments. Motion Tape (MT), a new fabric-based wearable sensor, addresses these issues by being low cost and portable. Despite these advantages, novelty and variability in sensor stability make the MT dataset small scale and inherent to noise. In this work, we propose the Motion-Tape Augmentation Inference Model (MT-AIM), a deep learning classification pipeline trained on MT data. In order to address the challenges of limited sample size and noise present within the MT dataset, MT-AIM leverages conditional generative models to generate synthetic MT data of a desired movement, as well as predicting joint kinematics as additional features. This combination of synthetic data generation and feature augmentation enables MT-AIM to achieve state-of-the-art accuracy in classifying lower back movements, bridging the gap between physiological sensing and movement analysis.