Rehabilitative robotics, particularly lower-limb exoskeletons (LLEs), have gained increasing importance in aiding patients regain ambulatory functions. One of the challenges in making these systems effective is the implementation of an assist-as-needed (AAN) control strategy that intervenes only when the patient deviates from the correct movement pattern. Equally crucial is the need for the LLE to exhibit "transparency" — minimising its interaction forces with the wearer to feel as natural as possible. This paper introduces a novel approach to transparency control based on a human-in-the-loop velocity optimisation framework. The proposed method employs torque data captured from past steps through a Series Elastic Actuator (SEA) to approximate the wearer’s intended future movements and computes a corresponding transparent velocity trajectory. The velocity commands are complemented by an Adaptive Frequency Oscillator (AFO) based position controller that leverages the periodic nature of human gait and is modified with a force sensor for increased reactiveness to human gait variations. This approach is experimentally evaluated against a standard zero-torque controller with a stationary single-degree-of-freedom knee exoskeleton test platform in a proof-of-concept study. Preliminary results indicate that combining adaptive oscillators with interaction force sensing can improve transparency compared to the conventional zero-torque controller, using force readings for position control and torque measurements for velocity optimisation and control.

To control wearable robotic systems, it is critical to obtain a prediction of the user's motion intent with high accuracy. Surface electromyography (sEMG) recordings have often been used as inputs for these devices, however bipolar sEMG electrodes are highly sensitive to their location. Positional shifts of electrodes after training gait prediction models can therefore result in severe performance degradation. This study uses high-density sEMG (HD-sEMG) electrodes to simulate various bipolar electrode signals from four leg muscles during steady-state walking. The bipolar signals were ranked based on the consistency of the corresponding sEMG envelope's activity and timing across gait cycles. The locations were then compared by evaluating the performance of an offline temporal convolutional network (TCN) that mapped sEMG signals to knee angles. The results showed that electrode locations with consistent sEMG envelopes resulted in greater prediction accuracy compared to hand-aligned placements (p$< $0.01). However, performance gains through this process were limited, and did not resolve the position shift issue. Instead of training a model for a single location, we showed that randomly sampling bipolar combinations across the HD-sEMG grid during training mitigated this effect. Models trained with this method generalized over all positions, and achieved 70% less prediction error than location specific models over the entire area of the grid. Therefore, the use of HD-sEMG grids to build training datasets could enable the development of models robust to spatial variations, and reduce the impact of muscle-specific electrode placement on accuracy.