School of Mechanical Science and Engineering, Jilin University, Jilin 130025, China.
J Healthc Eng. 2018 Feb 11;2018:6570617. doi: 10.1155/2018/6570617. eCollection 2018.
An original approach for noninvasive estimation of lower limb joint moments for analysis of STS rehabilitation training with only inertial measurement units was presented based on a piecewise three-segment STS biomechanical model and a double-sensor difference based algorithm. Joint kinematic and kinetic analysis using a customized wearable sensor system composed of accelerometers and gyroscopes were presented and evaluated compared with a referenced camera system by five healthy subjects and five patients in rehabilitation. Since there is no integration of angular acceleration or angular velocity, the result is not distorted without offset and drift. Besides, since there are no physical sensors implanted in the lower limb joints based on the algorithm, it is feasible to noninvasively analyze STS kinematics and kinetics with less numbers and types of inertial sensors than those mentioned in other methods. Compared with the results from the reference system, the developed wearable sensor system is available to do spatiotemporal analysis of STS task with fewer sensors and high degree of accuracy, to apply guidance and reference for rehabilitation training or desired feedback for the control of powered exoskeleton system.
提出了一种基于分段式三关节下肢生物力学模型和基于双传感器差值的算法,利用仅由加速度计和陀螺仪组成的定制可穿戴传感器系统,对下肢关节运动学和动力学进行分析,并与参考摄像机系统进行了比较。该系统由五名健康受试者和五名康复中的患者进行评估。由于没有角加速度或角速度的积分,因此结果不会因偏移和漂移而失真。此外,由于该算法没有在下肢关节中植入物理传感器,因此与其他方法相比,使用较少数量和类型的惯性传感器就可以实现非侵入式分析 STS 运动学和动力学。与参考系统的结果相比,开发的可穿戴传感器系统可以使用较少的传感器进行 STS 任务的时空分析,并具有较高的准确性,为康复训练提供指导和参考,或者为动力外骨骼系统的控制提供期望的反馈。