2025年2月7日 · Markerless vision-based human pose estimation (HPE) is a promising avenue towards scalable data collection in rehabilitation. Deploying this technology will require self-contained systems able to process data efficiently and accurately.

2024年12月14日 · This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models.

2025年2月7日 · The aims of this work are to (1) Determine how depth data affects lightweight monocular red-green-blue (RGB) HPE performance (accuracy and speed), to inform sensor selection and (2) Validate HPE models using data from individuals with physical impairments.

2025年3月16日 · A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions. Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb ...

2025年3月6日 · Accurate and reliable human pose estimation (HPE) is essential in interactive systems, particularly for applications requiring personalized adaptation, such as controlling cooperative robots and wearable exoskeletons, especially for healthcare monitoring equipment.

HPE methods must be validated for their feasibility and accuracy to measure the kinematics of persons with disabilities within the constraints of scalable deployment in healthcare environments.

2024年12月14日 · This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models.

The IJR module leverages the prior limb information for peripheral joint refinement. Extensive experimental results show that HopFIR outperforms the SOTA methods by a large margin, with a mean per-joint position error (MPJPE) on the Human3.6M dataset of 32.67 mm.

In this paper, the merits of both multiview images and wearable IMUs are combined to enhance the process of 3D HPE. We build upon a state-of-the-art baseline while introducing three novelties....

To solve this problem, we present a simple yet effective HPE network called limb direction cues (LDCs) aware network (LDCNet) with LDCs and differentiated Cauchy labels, which can efficiently suppress uncertainties and prevent deep networks from over-fitting uncertain keypoint positions.