Vision-based vs. IMU-based upper-limb pose estimation in assisted dressing: a comparative study of positional accuracy and kinematic fidelity.

Authors

Yasmin Rafiq, Shenglin Wang, Mohammed Al-Nuaimi, Lyudmila Mihaylova, Robert M Hierons, Sanja Dogramadzi

Published in

Frontiers in robotics and AI. Volume 13. Pages 1844439. Epub Jul 01, 2026.

Abstract

Accurate estimation of upper-limb kinematics is essential for applications such as rehabilitation assessment and assistive robotics, yet remains challenging in real-world scenarios involving occlusion and physical human interaction. While vision-based pose estimation methods have advanced significantly, their ability to recover reliable joint kinematics under such conditions remains unclear. This paper presents a systematic comparison of vision-based and wearable sensing approaches for upper-limb pose estimation during assisted dressing tasks. A monocular RGB-based convolutional neural network (CNN) and a temporally smoothed variant (CNN_temporal) are evaluated alongside a wearable IMU-based reconstruction method. All approaches are compared against an inverse kinematics (IK) reference derived from VICON motion capture data using participant-specific kinematic models. Performance is assessed using both positional error, measured via global and shoulder-centred mean per-joint position error (MPJPE), and kinematic agreement, measured via elbow flexion/extension angle error. Experiments on a real-world dataset of assisted dressing trials, involving an occupational therapist and three participants, demonstrate that IMU-based estimation provides consistently accurate and stable joint-angle reconstruction (e.g., $\sim 12°$ mean absolute error). In contrast, vision-based methods achieve reasonable positional accuracy (MPJPE $\sim$ 0.20 m) but exhibit substantially larger errors in joint-angle estimation (often exceeding 80 $°$ ), particularly under occlusion. Temporal smoothing improves positional consistency but does not preserve kinematic fidelity. These results highlight a fundamental limitation of current vision-based approaches for tasks requiring accurate joint kinematics. The findings suggest that integrating inertial sensing or incorporating biomechanical constraints may be necessary to achieve reliable pose estimation in real-world assistive scenarios.

PMID:
42460385
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.