Laboratoire de Recherche en Innovation Ouverte en Technologie de la Santé, Centre de Recherche CRCHUM, Montreal, QC H2X 0A9, Canada.
Département de Génie des Systèmes, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada.
Sensors (Basel). 2024 Sep 29;24(19):6307. doi: 10.3390/s24196307.
Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein-Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments.
手动轮椅推进代表着一种重复且受限的任务，主要导致脊髓损伤患者关节损伤的发展。主要原因之一是在推进周期中肩部承受的负荷。此外，肩部的负荷与手柄水平处的力和力矩高度相关。本研究的主要目的是使用每个手柄的单个惯性测量单元来估计轮椅推进过程中手柄的反作用力和力矩。这里提出了两种方法：首先，使用基于 Hammerstein-Wiener (HW) 建模方法的非线性传递函数识别方法。后者在系统工程建模方法中代表典型的多输入单输出。其次，提出了一种称为 BiLSTM 的递归神经网络的特定变体来预测手柄水平的力和力矩的时间序列数据。11 名受试者参与了这项线性推进协议研究，同时通过动态平台测量力和力矩。两个输入信号是腕关节的线性加速度和角速度。通过两种方法估计水平、垂直和矢状面力矩。BiLSTM 和 HW 的水平力的平均绝对误差 (MAE) 分别为 6.10 N 和 4.30 N。垂直方向的结果显示 BiLSTM 和 HW 的 MAE 分别为 5.91 N 和 7.59 N。最后，矢状面力矩的 MAE 从 0.96 Nm（BiLSTM）变化到 1.09 Nm（HW 模型）。这两种方法在 MAE 方面似乎相似，可以认为是准确的，因为据报道，动态平台的不确定性量级为水平和垂直力为 2.2 N，矢状面力矩为 2.24 Nm。然而，应当注意的是，HW 需要了解每个受试者的平均力和模式，而 BiLSTM 方法不涉及平均模式，这表明其在时间序列数据预测方面具有优越性。本研究提供的结果表明，在手推轮椅推进过程中，在生态环境中测量手柄水平处的动态力是可行的。
