Prediction of transition between locomotion modes (e.g. moving from flat ground to stairs, etc) is vital for optimal interface with lower limb assistive technologies such as exoskeletons and prostheses. Inertial and bipolar electromyography (EMG) sensors have been investigated, but accuracy for clinical utility remains unresolved. This shortfall may be attributed to their limited capacity to detect subtle changes in muscle activations, particularly during the early stages of locomotion transitions (e.g., near the toe-off). In this study, we examined the effectiveness of two high-density surface electromyography (HDsEMG) sensors in detecting muscle activation changes during stair-related transitions. The results revealed that compared to bipolar EMG on the same muscles, HDsEMG-based methods increased transition prediction accuracy significantly from 70.2% to 91.1% when predicting at toe-off and from 89.8% to 99.2% when predicting with a delay of 400-ms relative to toe-off. This demonstrated the superior ability of HDsEMG to capture subtle muscle activation changes, especially during early transition stages. We also found reducing the electrode count to 21 per muscle only minimally impacted performance (88.3% accuracy at toe-off). This suggests distributing the same total number of electrodes across more muscles could potentially further improve prediction accuracy without increasing computational load. Moreover, by implementing image-inpainting signal processing, HDsEMG demonstrated robustness against the common issue of electrode signal loss. Even with 30% electrode detachment, prediction accuracy decreased only by 3%. We argue that HDsEMG offers a promising solution to bridge the gap in locomotion transition prediction for interface with assistive technology.

Functional Electrical Stimulation (FES), a key therapy for improving extremity function (e.g., in post-stroke patients), is limited by rapid FES-induced muscle fatigue. Additionally, Electromyography (EMG) monitoring is significantly compromised by FES artifacts. Mechanomyography (MMG), directly immune to such electrical FES artifacts, offers a promising alternative for fatigue estimation; however, its quantitative use for closed-loop FES remains underdeveloped. This study validated an MMG-based FES fatigue assessment system, introducing a novel wearable sensor (pressure P_MMG, microphone M_MMG) and an MMG-driven Tibialis Anterior (TA) musculotendon model with an MMG-derived fatigue index. An isometric FES fatigue protocol was conducted on control ($N=15$) and post-stroke ($N=3$) participants, recording force and MMG signals. P_MMG Mean Value (MV) signals consistently decreased with fatigue, showing strong average Pearson correlations ($\bar{r}$) with force decline in both control ($\bar{r}=0.740$) and stroke ($\bar{r}=0.928$) groups ($p \leq 0.005$). Conversely, M_MMG signals exhibited inconsistent trends and weaker force correlations, largely due to non-monotonic behavior in many participants. The P_MMG MV-driven model accurately predicted force decline, achieving mean coefficients of determination ($R^{2}$) of 0.741 (control) and 0.774 (stroke), with strong prediction correlations ($\bar{r} > 0.87, p < 0.01$). Model predictions utilizing M_MMG signals were successful only for participant subsets with consistent signal trends. The pressure-based P_MMG sensor provided a robust, non-invasive FES-induced fatigue indicator. The P_MMG-driven model allows continuous estimation of force capacity decline, promising for closed-loop FES to optimize rehabilitation.

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.

It is well-known that the tendon-driven robotic manipulator plays an important role in robotic-assisted minimally invasive surgery (MIS). However, due to the intrinsic nonlinearities, uncertainties, slack and hysteresis introduced by the tendon-driven actuation, the tendon-driven robotic manipulator is difficult to model and control when compared with the traditional actuation styles. To serve the modeling purpose, in this paper, the deep-learning-based intelligent modeling of inverse kinematics in the snake-like tendon-driven surgical instrument is presented. In the proposed approach the Deep Recurrent Neural Network (DRNN) with Long Short-Term Memory (LSTM) architecture is adopted to memorize and identify the nonlinear inverse kinematics of the tendon-driven surgical instrument through the history of the motor and tip positions. To collect highly reliable data to train the DRNN, the experiment to generate training data is carefully designed with the consideration of the stainless tendon characters and motor limitations. During the designed controller movements, the kinematics data is obtained by recording the motor positions and the tip positions. Besides, it is noticed that there are correlations of the sequential data samples, which could significantly reduce the modeling accuracy. To remove the correlations and improve the modeling performance, the correlations of the sequential data samples are removed by modifying the training processes. Modeling results and detailed discussions verified the effectiveness of the proposed approach.

This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have one dominant frequency at the vertical perturbation frequency of the mobile robot and one with distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.