Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices.
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Date
2025-01-22
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI (Basel, Switzerland)
Rights
CC BY 4.0
(c) 2025 The Author/s
(c) 2025 The Author/s
Abstract
Traditional limb kinematic analysis relies on manual goniometer measurements. With computer vision advancements, integrating RGB cameras can minimize manual labor. Although deep learning-based cameras aim to offer the same ease as manual goniometers, previous approaches have prioritized accuracy over efficiency and cost on PC-based devices. Nevertheless, healthcare providers require a high-performance, low-cost, camera-based tool for assessing upper and lower limb range of motion (ROM). To address this, we propose a lightweight, fast, deep learning model to estimate a human pose and utilize predicted joints for limb ROM measurement. Furthermore, the proposed model is optimized for deployment on resource-constrained edge devices, balancing accuracy and the benefits of edge computing like cost-effectiveness and localized data processing. Our model uses a compact neural network architecture with 8-bit quantized parameters for enhanced memory efficiency and reduced latency. Evaluated on various upper and lower limb tasks, it runs 4.1 times faster and is 15.5 times smaller than a state-of-the-art model, achieving satisfactory ROM measurement accuracy and agreement with a goniometer. We also conduct an experiment on a Raspberry Pi, illustrating that the method can maintain accuracy while reducing equipment and energy costs. This result indicates the potential for deployment on other edge devices and provides the flexibility to adapt to various hardware environments, depending on diverse needs and resources.
Description
Keywords
RGB camera, clinical assessment, edge device, fast deep learning model, joint range of motion, pose estimation, Humans, Range of Motion, Articular, Biomechanical Phenomena, Deep Learning, Neural Networks, Computer, Lower Extremity
Citation
Yan X, Zhang L, Liu B, Qu G. (2025). Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices.. Sensors (Basel). 25. 3. (pp. 627-).