An Open-source and Wearable System for Estimating 3D Human Motion in Real-time

Objective: Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson's disease. Optical motion capture systems are the standard for estimating kinematics, but the equipment is expensive and requires a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Many wearable sensor systems require a computer in close proximity and use proprietary software, limiting experimental reproducibility. Methods: Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller. Results: We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task. The open-source software and hardware are scalable, tracking 1 to 14 body segments, with one sensor per segment. A musculoskeletal model and inverse kinematics solver estimate Kinematics in real-time. The computation frequency depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment. Significance: The OpenSenseRT system is validated against optical motion capture, low-cost, and simple to replicate, enabling movement analysis in clinics, homes, and free-living settings.

研究目标：人体运动分析对于骨关节炎、脑卒中、帕金森病等病症的运动障碍诊断与康复指导至关重要。光学运动捕捉系统是运动学估计的金标准，但该设备成本高昂且需要预设专用空间。尽管可穿戴传感器系统可在任意环境中完成运动学估计，但现有系统的精度通常低于光学运动捕捉系统。此外，多数可穿戴传感器系统需要近距离部署计算机，并使用专有软件，这限制了实验的可重复性。研究方法：本文提出OpenSenseRT——一款开源可穿戴系统，通过惯性测量单元（inertial measurement unit, IMU）与便携式微控制器实现上下肢运动学的实时估计。研究结果：本研究将OpenSenseRT系统与光学运动捕捉系统进行对比，结果显示，在3分钟步行任务中，5个下肢关节角度的平均均方根误差（Root Mean Square Error, RMSE）为4.4度；在Fugl-Meyer评估任务中，8个上肢关节角度的平均RMSE为5.6度。该系统的软硬件均具备可扩展性，可追踪1至14个身体节段，每个节段配备一枚传感器。系统通过肌肉骨骼模型（musculoskeletal model）与逆运动学求解器（inverse kinematics solver）实现运动学的实时估计。计算频率取决于追踪的节段数量，但足以满足多数常见任务的实时测量需求；例如，该系统可实时以30Hz的频率追踪7个节段。本系统采用通用商用部件构建，总成本约为100美元，每额外追踪一个节段需增加20美元成本。研究意义：经与光学运动捕捉系统对标验证，OpenSenseRT系统具备低成本、易复现的特点，可在临床诊室、居家环境与自然活动场景中开展运动分析工作。