Multi-feature Compensatory Motion Analysis for Reaching Motions Over a Discretely Sampled Workspace

• URL: http://arxiv.org/abs/2409.05871v1
• Date: Fri, 23 Aug 2024 19:39:08 GMT
• Title: Multi-feature Compensatory Motion Analysis for Reaching Motions Over a Discretely Sampled Workspace
• Authors: Qihan Yang, Yuri Gloumakov, Adam J. Spiers,
• Abstract summary: The absence of functional arm joints, such as the wrist, in upper extremity prostheses leads to compensatory motions. This work analysed compensatory motions in the final pose of subjects reaching across a discretely sampled 7*7 2D grid of targets.
• Score: 5.004501184476518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The absence of functional arm joints, such as the wrist, in upper extremity prostheses leads to compensatory motions in the users' daily activities. Compensatory motions have been previously studied for varying task protocols and evaluation metrics. However, the movement targets' spatial locations in previous protocols were not standardised and incomparable between studies, and the evaluation metrics were rudimentary. This work analysed compensatory motions in the final pose of subjects reaching across a discretely sampled 7*7 2D grid of targets under unbraced (normative) and braced (compensatory) conditions. For the braced condition, a bracing system was applied to simulate a transradial prosthetic limb by restricting participants' wrist joints. A total of 1372 reaching poses were analysed, and a Compensation Index was proposed to indicate the severity level of compensation. This index combined joint spatial location analysis, joint angle analysis, separability analysis, and machine learning (clustering) analysis. The individual analysis results and the final Compensation Index were presented in heatmap format to correspond to the spatial layout of the workspace, revealing the spatial dependency of compensatory motions. The results indicate that compensatory motions occur mainly in a right trapezoid region in the upper left area and a vertical trapezoid region in the middle left area for right-handed subjects reaching horizontally and vertically. Such results might guide motion selection in clinical rehabilitation, occupational therapy, and prosthetic evaluation to help avoid residual limb pain and overuse syndromes.

Related papers

• Occluded Human Pose Estimation based on Limb Joint Augmentation [14.36131862057872]
  We propose an occluded human pose estimation framework based on limb joint augmentation to enhance the generalization ability of the pose estimation model on the occluded human bodies. To further enhance the localization ability of the model, this paper constructs a dynamic structure loss function based on limb graphs to explore the distribution of occluded joints.
  arXiv  Detail & Related papers  (2024-10-13T15:48:24Z)
• Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms [0.5530212768657544]
  We propose a data-driven method to learn realistic anatomically constrained upper-limb range of motion boundaries from motion capture data. We also propose an impairment index (II) metric that offers a quantitative assessment of capability/impairment when comparing healthy and impaired arms.
  arXiv  Detail & Related papers  (2023-11-17T17:14:42Z)
• Strengths and Weaknesses of 3D Pose Estimation and Inertial Motion Capture System for Movement Therapy [0.0]
  3D pose estimation offers the opportunity for fast, non-invasive, and accurate motion analysis. We investigate the accuracy of the state-of-the-art 3D position estimation approach MeTrabs, compared to the established inertial sensor system MTw Awinda.
  arXiv  Detail & Related papers  (2023-06-01T20:35:06Z)
• Improved Trajectory Reconstruction for Markerless Pose Estimation [0.0]
  Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views. We tested different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy. We found that using a top-down keypoint detector and reconstructing trajectories with an implicit function enabled accurate, smooth and anatomically plausible trajectories.
  arXiv  Detail & Related papers  (2023-03-04T13:16:02Z)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• Vogtareuth Rehab Depth Datasets: Benchmark for Marker-less Posture Estimation in Rehabilitation [55.41644538483948]
  We propose two rehabilitation-specific pose datasets containing depth images and 2D pose information of patients performing rehab exercises. We use a state-of-the-art marker-less posture estimation model which is trained on a non-rehab benchmark dataset. We show that our dataset can be used to train pose models to detect rehab-specific complex postures.
  arXiv  Detail & Related papers  (2021-08-23T16:18:26Z)
• Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation [116.07661813869196]
  We propose to combine ideas from adversarial training and motion modelling to tap into unlabeled videos. We show that an adversarial leads to better properties of the hand pose estimator via semi-supervised training on unlabeled video sequences. The main advantage of our approach is that we can make use of unpaired videos and joint sequence data both of which are much easier to attain than paired training data.
  arXiv  Detail & Related papers  (2021-06-10T17:50:19Z)
• Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection [58.984536305767996]
  We propose a representation learning-inspired approach for automated vertebral fracture detection. We present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme. On a publicly available spine dataset, the proposed loss function achieves a fracture detection F1 score of 81.5%.
  arXiv  Detail & Related papers  (2020-08-18T10:03:45Z)
• Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee [6.7461735822055715]
  Involuntary motion during CT scans of the knee causes artifacts in the reconstructed volumes making them unusable for clinical diagnosis. We propose to attach an inertial measurement unit (IMU) to the leg of the subject in order to measure the motion during the scan and correct for it.
  arXiv  Detail & Related papers  (2020-07-09T09:26:27Z)
• Deep Negative Volume Segmentation [60.44793799306154]
  We propose a new angle to the 3D segmentation task: segment empty spaces between all the tissues surrounding the object. Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation. We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine.
  arXiv  Detail & Related papers  (2020-06-22T16:55:23Z)
• Appearance Learning for Image-based Motion Estimation in Tomography [60.980769164955454]
  In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Patient motion corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object.
  arXiv  Detail & Related papers  (2020-06-18T09:49:11Z)

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.