Molinaro Dean D, Kang Inseung, Camargo Jonathan, Young Aaron J
Institute for Robotics and Intelligent Machines (IRIM) and the Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron. 2020 Nov-Dec;2020:791-796. doi: 10.1109/biorob49111.2020.9224334. Epub 2020 Oct 15.
Machine learning (ML) algorithms present an opportunity to estimate joint kinetics using a limited set of mechanical sensors. These estimates could be used as a continuous reference signal for exoskeleton control, able to modulate exoskeleton assistance in real-world environments. In this study, sagittal plane biological hip torque during level ground, incline and decline walking was calculated using inverse dynamics of human subject data. Subsequently, this torque was estimated using neural network (NN) and XGBoost ML models. Model inputs consisted solely of mechanical sensor data onboard a robotic hip exoskeleton. These results were compared to a baseline method of estimating hip torque as the mean torque profile during ambulation. On average across conditions, the NN and XGBoost models estimated biological hip torque with an RMSE of 0.116±0.015 and 0.108±0.011 Nm/kg, respectively, which was significantly less than the baseline estimation that had an RMSE of 0.300±0.145 Nm/kg (p<0.05). Fitting the baseline method to ambulation mode specific data significantly reduced overall RMSE by 59.3%; however, the ML models were still significantly better than the baseline method (p<0.05). These results show that machine learning algorithms can estimate biological hip torque using only mechanical sensors onboard a hip exoskeleton better than simply using an average torque profile. This suggests that these estimation models could be suitable for modulating exoskeleton assistance. Additionally, no evidence suggested the need to train separate ML models for each ambulation mode as estimation RMSE was not significantly different across unified and separated ML models.
机器学习（ML）算法为利用有限的一组机械传感器估计关节动力学提供了契机。这些估计值可作为外骨骼控制的连续参考信号，能够在现实环境中调节外骨骼辅助。在本研究中，利用人体受试者数据的逆动力学计算了在平地上、上坡和下坡行走时矢状面生物髋关节扭矩。随后，使用神经网络（NN）和XGBoost机器学习模型对该扭矩进行估计。模型输入仅包括机器人髋关节外骨骼上的机械传感器数据。将这些结果与一种将髋关节扭矩估计为步行过程中平均扭矩曲线的基线方法进行比较。在所有条件下，NN和XGBoost模型估计生物髋关节扭矩的均方根误差（RMSE）分别为0.116±0.015和0.108±0.011 Nm/kg，显著低于基线估计的0.300±0.145 Nm/kg（p<0.05）。将基线方法拟合到特定步行模式的数据可使总体RMSE显著降低59.3%；然而，机器学习模型仍显著优于基线方法（p<0.05）。这些结果表明，机器学习算法仅使用髋关节外骨骼上的机械传感器就能比简单使用平均扭矩曲线更好地估计生物髋关节扭矩。这表明这些估计模型可能适用于调节外骨骼辅助。此外，没有证据表明需要为每种步行模式训练单独的机器学习模型，因为统一和分离的机器学习模型的估计RMSE没有显著差异。
