Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices.(MDPI (Basel, Switzerland), 2025-01-22) Yan X; Zhang L; Liu B; Qu G; Amerini I; Russo P; Di Ciaccio FTraditional 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.Item Astragalosides Supplementation Enhances Intrinsic Muscle Repair Capacity Following Eccentric Exercise-Induced Injury(MDPI (Basel, Switzerland), 2022-10) Yeh T-S; Lei T-H; Barnes MJ; Zhang LAstragalosides have been shown to enhance endurance exercise capacity in vivo and promote muscular hypertrophy in vitro. However, it remains unknown whether astragalosides supplementation can alter inflammatory response and enhance muscle recovery after damage in humans. We therefore aimed to evaluate the effect of astragalosides supplementation on muscle's intrinsic capacity to regenerate and repair itself after exercise-induced damage. Using a randomized double-blind placebo-controlled cross-over design, eleven male participants underwent 7 days of astragalosides supplementation (in total containing 4 mg of astragalosides per day) or a placebo control, following an eccentric exercise protocol. Serum blood samples and variables related to muscle function were collected prior to and immediately following the muscle damage protocol and also at 2 h, and 1, 2, 3, 5, and 7 days of the recovery period, to assess the pro-inflammatory cytokine response, the secretion of muscle regenerative factors, and muscular strength. Astragalosides supplementation reduced biomarkers of skeletal muscle damage (serum CK, LDH, and Mb), when compared to the placebo, at 1, 2, and 3 days following the muscle damage protocol. Astragalosides supplementation suppressed the secretion of IL-6 and TNF-α, whilst increasing the release of IGF-1 during the initial stages of muscle recovery. Furthermore, following astragaloside supplementation, muscular strength returned to baseline 2 days earlier than the placebo. Astragalosides supplementation shortens the duration of inflammation, enhances the regeneration process and restores muscle strength following eccentric exercise-induced injury.