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

arXiv: https://arxiv.org/abs/2602.11465 · HTML: https://mlsanity.com/paper/2602.11465/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 14 of 14)

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- [STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition](https://mlsanity.com/paper/2303.18177/) (2023)
- [Clustering time series under the Fr'echet distance](https://mlsanity.com/paper/1512.04349/) (2015)
- [Inertial Sensors for Human Motion Analysis: A Comprehensive Review](https://mlsanity.com/paper/2401.12919/) (2024)
- [Deep learning empowered sensor fusion boosts infant movement classification](https://mlsanity.com/paper/2406.09014/) (2024)
- [Using Dimension Reduction to Improve the Classification of High-dimensional Data](https://mlsanity.com/paper/1505.06907/) (2015)
- [Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings](https://mlsanity.com/paper/2209.11481/) (2022)
- [Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition](https://mlsanity.com/paper/2411.15655/) (2024)

## Abstract (arXiv)

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.