RESUMEN
Inverse dynamics is a common tool for determining human joint torques during walking. The traditional approaches rely on ground reaction force and kinematics measurements prior to analysis. A novel real-time hybrid method is proposed in this work by integrating a neural network and dynamic model that only requires kinematic data. An end-to-end neural network for direct joint torque estimation is also developed based on kinematic data. The neural networks are trained on a variety of walking conditions, including starting and stopping, sudden speed changes, and asymmetrical walking. The hybrid model is first tested in a detailed dynamic gait simulation (OpenSim) which results in root mean square errors less than 5 N.m and a correlation coefficient of greater than 0.95 for all the joints. Experiments demonstrate that the end-to-end model on average outperforms the hybrid model across the whole test when compared to the gold standard approach which requires both kinetic and kinematic information. The two torque estimators are also tested on one participant wearing a lower limb exoskeleton. In this case, the hybrid model (R 0.84) has significantly better performance than the end-to-end neural network (R 0.59). This indicates that the hybrid model is better applicable to scenarios which differ from the training data.
Asunto(s)
Marcha , Caminata , Humanos , Torque , Extremidad Inferior , Redes Neurales de la Computación , Fenómenos BiomecánicosRESUMEN
An accurate real-time gait phase estimator for normal and asymmetric gait is developed by training and testing a time-delay neural network on gait data collected from six participants during treadmill walking. The trained model can generate smooth and highly accurate predictions of the gait phase with a root mean square error of less than 3.48% and 4.31% in normal and asymmetric gait, respectively. The coefficient of determination between the estimated and target phase is greater than 99% for all subjects with both normal and asymmetric gait. The proposed gait estimator also exhibits precise heel-strike event detection with an RMSE of 2.56% and 3.70% in normal and asymmetric gait, respectively. A spatial impedance controller is then employed and tested based on the estimated gait phase of a new participant. Obtained results confirm that the controller provided assistance in coordination with the user's motion both in normal and asymmetric gait conditions. The estimated gait phase is compared in the case of walking without and with the exoskeleton in passive and active modes, indicating persistent accuracy of the gait phase estimator regardless of the walking conditions.
Asunto(s)
Dispositivo Exoesqueleto , Marcha , Fenómenos Biomecánicos , Prueba de Esfuerzo , Talón , Humanos , CaminataRESUMEN
Virtual Energy Regulator (VER) is a time independent controller that can generate stable limit cycles in lower-limb exoskeleton devices. In this work, we apply VER to control a lower-limb exoskeleton for assistive walking. We design two different limit cycles for hip and knee joints to assist the user during overground walking with the Indego explorer lower-limb exoskeleton. We tested the designed VER on a single participant for overground walking at a self-selected speed. Interestingly, due to VER time-independent nature, it can properly coordinate with the user's motions and produce mechanically stable overground walking in which the user can walk overground without a walker or crutches. The resultant gait is also more similar to a normal gait with improved range of motion compared to cases without controller; range of motion improved from $42.9 \pm 4.8{deg}$ and $44.9 \pm 4.9 {deg}$ to $46.6 \pm 1.3 {deg}$ and $63.0 \pm 6.8 {deg}$ at hip and knee joints, respectively. Especially, for the knee joint, the user is able to fully extend her knee during stance phase only when the VER is in the loop. In VER, the radius of each desired limit cycle is a function of phase. Accordingly, during walking, the internal phase of the VER is a monotonically increasing parameter that can be considered as a candidate for real-time gait phase estimation and heel-strike event detection. Hence, for gait phase estimation, VER relies only on a single joint position provided by the exoskeleton.
Asunto(s)
Dispositivo Exoesqueleto , Fenómenos Biomecánicos , Muletas , Femenino , Marcha , Humanos , Extremidad Inferior , CaminataRESUMEN
An ultra-robust accurate gait phase estimator is developed by training a time-delay neural network (D67) on data collected from the hip and knee joint angles of 14 participants during treadmill and overground walking. Collected data include normal gait at speeds ranging from 0.1m/s to 1.9m/s and conditions such as long stride, short stride, asymmetric walking, stop-start, and abrupt speed changes. Spatial analysis of our method indicates an average RMSE of 1.74±0.23% and 2.35±0.52% in gait phase estimation of test participants in the treadmill and overground walking, respectively. The temporal analysis reveals that D67 detects heel-strike events with an average MAE of 1.70±0.54% and 2.74±0.92% of step duration on test participants in the treadmill and overground walking, respectively. Both spatial and temporal performances are uniform across participants and gait conditions. Further analyses indicate the robustness of the D67 to smooth and abrupt speed changes, limping, variation of stride length, and sudden start or stop of walking. The performance of the D67 is also compared to the state-of-the-art techniques confirming the superior and comparable performance of the D67 to techniques without and with a ground contact sensor, respectively. The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton.