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

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.,