The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
Sensors (Basel). 2022 Feb 23;22(5):1731. doi: 10.3390/s22051731.
Timely and reliable identification of control phases is functional to the control of a powered robotic lower-limb prosthesis. This study presents a commercial energy-store-and-release foot prosthesis instrumented with a multimodal sensory system comprising optoelectronic pressure sensors (PS) and IMU. The performance was verified with eight healthy participants, comparing signals processed by two different algorithms, based on PS and IMU, respectively, for real-time detection of heel strike (HS) and toe-off (TO) events and an estimate of relevant biomechanical variables such as vertical ground reaction force (vGRF) and center of pressure along the sagittal axis (CoPy). The performance of both algorithms was benchmarked against a force platform and a marker-based stereophotogrammetric motion capture system. HS and TO were estimated with a time error lower than 0.100 s for both the algorithms, sufficient for the control of a lower-limb robotic prosthesis. Finally, the CoPy computed from the PS showed a Pearson correlation coefficient of 0.97 (0.02) with the same variable computed through the force platform.
及时可靠地识别控制阶段对于电动下肢假肢的控制至关重要。本研究介绍了一种带有多模态传感器系统的商业储能和释放式足部假肢,该系统包括光电压力传感器 (PS) 和惯性测量单元 (IMU)。该性能通过 8 名健康参与者进行了验证,比较了基于 PS 和 IMU 的两种不同算法处理的信号,用于实时检测足跟触地 (HS) 和脚趾离地 (TO) 事件,并估计相关生物力学变量,如垂直地面反作用力 (vGRF) 和沿矢状轴的压力中心 (CoPy)。两种算法的性能均与力台和基于标记的立体摄影运动捕捉系统进行了基准测试。对于两种算法,HS 和 TO 的估计时间误差均低于 0.100s,足以控制下肢机器人假肢。最后,从 PS 计算得到的 CoPy 与通过力台计算得到的相同变量之间的 Pearson 相关系数为 0.97(0.02)。