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Acquiring the human’s musculoskeletal movements is pivotal in conducting physiotherapeutic studies, rehabilitation exercises and, designing assistive devices to cater mobility impairments. Existing studies are generally more focused on measuring a specific phenomenon and/or lack the diversity needed to cover a range of human activities. In this context, the research presented herein is aimed to develop a low-cost wearable sensory system, having a diverse set of sensors, which can acquire data of healthy and rehabilitated subjects alike during diverse locomotor activities in both indoor and outdoor environments. The system consists of twenty wearable sensors, and two off-the-shelf NI’s myRIO microcontroller boards. The statistical analysis showed no significant difference between and among the subjects for various locomotor activities (P-values < 0.05), hence, showing the system’s reliability and reproducibility. Gait-event identification (i.e., heel contact/toe off) has also been evaluated and showed promising results (overall time difference= ±50ms during level ground and ramp activities).

获取人体肌肉骨骼运动数据，对于开展物理治疗研究、康复训练以及设计面向运动功能障碍人群的辅助器具至关重要。现有研究大多仅聚焦于单一现象的测量，或缺乏覆盖多样化人体活动所需的数据多样性。鉴于此，本研究旨在开发一套低成本可穿戴传感系统：该系统搭载多样化传感器，可在室内外多种运动活动场景下，采集健康受试者与康复后受试者的运动数据。该系统包含20个可穿戴传感器，以及两块市售NI myRIO微控制器板。统计分析结果显示，针对各类运动活动，不同受试者之间均无显著性差异（P值<0.05），由此验证了该系统的可靠性与可复现性。步态事件识别（Gait-event identification，即足跟触地/脚尖离地）已得到评估并展现出优异性能：平地与坡道活动中的总时间误差为±50ms。