In recent decades, there has been an increasing interest in the use of robotic powered exoskeletons to assist patients with movement disorders in rehabilitation and daily life. Providing assistive torque that compensates for the user’s remaining muscle contributions is a growing and challenging field within exoskeleton control. In this article, ankle joint torques were estimated using electromyography (EMG)-driven neuromusculoskeletal (NMS) model and an artificial neural network (ANN) model in seven movement tasks, including fast walking, slow walking, self-selected speed walking, and isokinetic dorsi/plantar flexion at 60◦/s and 90◦/s . In each method, EMG signals and ankle joint angles were used as input, the models were trained with data from 3-D motion analysis, and ankle joint torques were predicted. Six cases using different motion trials as calibration (for the NMS model)/training (for the ANN) were devised, and the agreement between the predicted and measured ankle joint torques was computed. We found that the NMS model could overall better predict ankle joint torques from EMG and angle data than the ANN model with some exceptions; the ANN predicted ankle joint torques with better agreement when trained with data from the same movement. The NMS model predicted ankle joint torque best when calibrated with trials during which EMG reached maximum levels, whereas the ANN predicted well when trained with many trials and types of movements. In addition, the ANN prediction may become less reliable when predicting unseen movements. Detailed comparative studies of methods to predict ankle joint torque are crucial for determining strategies for exoskeleton control. Note to Practitioners—In exoskeleton control for strength augmentation applied in military, industry, and healthcare applications, providing assistive torque that compensates for the user’s remaining muscle contributions, is a challenging problem. This article predicted the ankle joint torques by electromyography (EMG)-driven neuromusculoskeletal (NMS) model and an artificial neural network (ANN) model in different movements. To the best of our knowledge, this is the first study comparing joint torque prediction performance of EMG-driven model to ANN. In the EMG-driven NMS model, mathematical equations were formulated to reproduce the transformations from EMG signal generation and joint angles to musculotendon forces and joint torques. A three-layer ANN was constructed with an adaptive moment estimation (Adam) optimization method to learn the relationships between the inputs (EMG signals and joint angles) and the outputs (joint torques). In the experiments, we estimated ankle joint torques in gait and isokinetic movements and compared the performance of methods to predict ankle joint torque, relating to how the methods have been calibrated/trained. The detailed analysis of the methods’ performance in predicting ankle joint torque can significantly contribute to determining which model to choose, and under which circumstances, and, thus, be of great benefit for exoskeleton rehabilitation controller design.

Exoskeletons are increasingly used in rehabilitation and daily life in patients with motor disorders after neurological injuries. In this paper, a realistic human knee exoskeleton model based on a physical system was generated, a human–machine system was created in a musculoskeletal modeling software, and human–machine interactions based on different assistive strategies were simulated. The developed human–machine system makes it possible to compute torques, muscle impulse, contact forces, and interactive forces involved in simulated movements. Assistive strategies modeled as a rotational actuator, a simple pendulum model, and a damped pendulum model were applied to the knee exoskeleton during simulated normal and fast gait. We found that the rotational actuator–based assistive controller could reduce the user's required physiological knee extensor torque and muscle impulse by a small amount, which suggests that joint rotational direction should be considered when developing an assistive strategy. Compared to the simple pendulum model, the damped pendulum model based controller made little difference during swing, but further decreased the user's required knee flexor torque during late stance. The trade-off that we identified between interaction forces and physiological torque, of which muscle impulse is the main contributor, should be considered when designing controllers for a physical exoskeleton system. Detailed information at joint and muscle levels provided in this human–machine system can contribute to the controller design optimization of assistive exoskeletons for rehabilitation and movement assistance.

Lower extremity powered exoskeletons help people with movement disorders to perform daily activities and are used increasingly in gait retraining and rehabilitation. Studies of powered exoskeletons often focus on technological aspects such as actuators, control methods, energy and effects on gait. Limited research has been conducted on how different mechanical design parameters can affect the user. In this paper, we study the effects of weight distributions of knee exoskeleton components on simulated muscle activities during three functional movements. Four knee exoskeleton CAD models were developed based on actual motor and gear reducer products. Different placements of the motor and gearbox resulted in different weight distributions. One unilateral knee exoskeleton prototype was fabricated and tested on 5 healthy subjects. Simulation results were compared to observed electromyography signals. Muscle activities varied among weight distributions and movements, wherein no one physical design was optimal for all movements. We describe how a powered exoskeleton's core components can be expected to affect a user's ability and performance. Exoskeleton physical design should ideally take the user's activity goals and ability into consideration.