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

• URL: http://arxiv.org/abs/2602.11465v1
• Date: Thu, 12 Feb 2026 00:51:41 GMT
• Title: Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning
• Authors: Jared Levy, Aarti Lalwani, Elijah Wyckoff, Kenneth J. Loh, Sara P. Gombatto, Rose Yu, Emilia Farcas,
• Abstract summary: Back pain is a pervasive issue affecting a significant portion of the population, often worsened by certain movements of the lower back.<n>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.<n> Motion Tape (MT), a new fabric-based wearable sensor, addresses these issues by being low cost and portable.<n>We propose the Motion-Tape Augmentation Inference Model (MT-AIM), a deep learning classification pipeline trained on MT data.
• Score: 18.9874861516187
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction [54.36564144414704]
  MeshMimic is an innovative framework that bridges 3D scene reconstruction and embodied intelligence to enable humanoid robots to learn coupled "motion-terrain" interactions directly from video.<n>By leveraging state-of-the-art 3D vision models, our framework precisely segments and reconstructs both human trajectories and the underlying 3D geometry of terrains and objects.
  arXiv  Detail & Related papers  (2026-02-17T17:09:45Z)
• PUMPS: Skeleton-Agnostic Point-based Universal Motion Pre-Training for Synthesis in Human Motion Tasks [44.19486142246208]
  Motion skeletons drive 3D character animation by transforming bone hierarchies, but differences in proportions or structure make motion data hard to transfer across skeletons.<n>Temporal Point Clouds (TPCs) offer an unstructured, cross-compatible motion representation.<n>We propose PUMPS, the primordial autoencoder architecture for TPC data.
  arXiv  Detail & Related papers  (2025-07-27T08:20:49Z)
• SSPINNpose: A Self-Supervised PINN for Inertial Pose and Dynamics Estimation [0.0]
  Inertial measurement units (IMUs) provide a minimally intrusive solution for capturing motion data.<n>Current real-time methods rely on supervised learning, where a ground truth dataset needs to be measured with laboratory measurement systems.<n>We propose SSPINNpose, a self-supervised, physics-informed neural network that estimates joint kinematics and kinetics directly from IMU data.
  arXiv  Detail & Related papers  (2025-06-13T13:47:27Z)
• Estimation of Head Motion in Structural MRI and its Impact on Cortical Thickness Measurements in Retrospective Data [4.072070248526498]
  Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI)<n>These artifacts can bias automated neuroanatomical metrics such as cortical thickness.<n>Here, we train a 3D convolutional neural network to estimate a summary motion metric in retrospective routine research scans.
  arXiv  Detail & Related papers  (2025-05-29T18:08:59Z)
• K2MUSE: A human lower limb multimodal dataset under diverse conditions for facilitating rehabilitation robotics [15.245241949892584]
  The K2MUSE dataset includes a comprehensive collection of multimodal data, comprising kinematic, kinetic, amplitude-mode ultrasound (AUS), and surface electromyography (sEMG) measurements.<n>This dataset offers a new resource for designing control frameworks for rehabilitation robots and conducting biomechanical analyses of lower limb locomotion.
  arXiv  Detail & Related papers  (2025-04-20T13:03:56Z)
• Validation of Human Pose Estimation and Human Mesh Recovery for Extracting Clinically Relevant Motion Data from Videos [79.62407455005561]
  Marker-less motion capture using human pose estimation produces results in-line with the results of both the IMU and MoCap kinematics.<n>While there is still room for improvement when it comes to the quality of the data produced, we believe that this compromise is within the room of error.
  arXiv  Detail & Related papers  (2025-03-18T22:18:33Z)
• Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals [0.8388591755871735]
  Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks. This paper presents a novel motion prediction framework that integrates three Inertial Measurement Units (IMUs) and eight surface Electromyography (sEMG) sensors. Our findings reveal that Convolutional Neural Networks (CNNs) slightly outperform Long Short-Term Memory (LSTM) networks on a dataset of five motion tasks.
  arXiv  Detail & Related papers  (2024-11-25T10:51:40Z)
• Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU) [0.0]
  The objective of the dataset is to pave the way towards affordable patient gross motor tracking solutions for daily life activities recognition and kinematic analysis. The novelty of dataset lies in: (i) the clinical relevance of the chosen movements, (ii) the combined utilization of affordable video and custom sensors, and (iii) the implementation of state-of-the-art tools for multimodal data processing of 3D body pose tracking and motion reconstruction.
  arXiv  Detail & Related papers  (2023-03-27T14:05:49Z)
• Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement [25.27559386977351]
  Body motion is one of the most significant sources of noise when attempting to recover the subtle cardiac pulse from a video. We adapt a neural video synthesis approach to augment videos for the task of remote photoplethys. We demonstrate a 47% improvement over existing inter-dataset results using various state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-21T17:51:23Z)
• Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase [72.01862340497314]
  We propose a task-agnostic deep learning method, namely Multi-scale Control Signal-aware Transformer (MCS-T) MCS-T is able to successfully generate motions comparable to those generated by the methods using auxiliary information.
  arXiv  Detail & Related papers  (2023-03-03T02:56:44Z)
• Transformer Inertial Poser: Attention-based Real-time Human Motion Reconstruction from Sparse IMUs [79.72586714047199]
  We propose an attention-based deep learning method to reconstruct full-body motion from six IMU sensors in real-time. Our method achieves new state-of-the-art results both quantitatively and qualitatively, while being simple to implement and smaller in size.
  arXiv  Detail & Related papers  (2022-03-29T16:24:52Z)
• Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
  We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics. Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
  arXiv  Detail & Related papers  (2021-04-29T00:11:32Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.