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.

Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergyinspired method of locomotion mode identification by fusing the electromyography data with two types of data from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From the finite state machine perspective, the enhanced method was used to systematically identify 2 static modes, 7 dynamic modes, and 27 transitions among them. In addition to the five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), we identified the transition between different walking speeds and modes of ramp walking at different inclination angles. Seven combinations of sensor fusion were conducted, on experimental data from 8 able-bodied adult subjects, and their classification accuracy and prediction time were compared. Prediction based on a fusion of electromyography and gyroscope (angular velocity) data predicted transitions earlier and with higher accuracy. All transitions and modes were identified with a total average classification accuracy of 94.5% with fused sensor data. For nearly all transitions, we were able to predict the next locomotion mode 300-500 ms prior to the step into that mode.

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.

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