Zhenhua Yu

Mobility impairments among older adults and individuals with neuromuscular weakness motivate the need for timely and adaptive exoskeleton assistance. This paper presents a human-in-the-loop muscle sensing and control system based on capacitive microphone sensors (CMS) that capture subtle mechanical muscle vibrations preceding observable motion. CMS signals were shown to occur 20–30 ms earlier than IMU-based kinematics, enabling anticipatory intent detection and feedforward assistive control. A five-sensor CMS array positioned over major thigh muscles is combined with a two-stage control strategy that integrates threshold-based pre-assist triggering and machine-learning-based torque refinement. Experiments across walking, stair ascent, sitting, and standing achieved over 90% classification accuracy under both non-fatigued and fatigued conditions with low latency. Robustness evaluations demonstrate stable CMS performance under realistic wearable perturbations, including perspiration and attachment variation. Extended experimental sessions (1–2 h) and preliminary feedback from five participants indicate comfortable wear and natural interaction. These results highlight the potential of CMS-based anticipatory sensing for practical wearable exoskeleton deployment in daily scenarios.

This paper presents a bio-inspired underwater whisker sensor for robust hydrodynamic disturbance detection and efficient signal analysis based on Physical Reservoir Computing (PRC). The design uses a tapered nylon spring with embedded accelerometers to achieve spatially distributed vibration sensing and frequency separation along the whisker. Towing-tank experiments and computational fluid dynamics simulations confirmed that the whisker effectively distinguishes vortex regimes across different fin angles and maintains Strouhal scaling with flow velocity, where higher speeds increase vibration intensity without affecting the dominant frequencies. Frequency-domain analysis, Shannon entropy, and machine learning further validated the sensing performance: vortex shedding frequencies were identified with less than 10% error, entropy captured the transition from coherent vortex streets to turbulence, and logistic regression achieved 86.0% classification accuracy with millisecond-level inference. Importantly, the sensing objective of this work is to characterize repeatable and physically interpretable hydrodynamic disturbances, which provide a controlled basis for evaluating how the tapered geometry encodes flow-induced vibrations. Under this scope, the demonstrated frequency–spatial decoupling and multi-modal responses show that structurally encoded whisker sensing provides a scalable and real-time solution for underwater perception, wake tracking, and turbulence-aware navigation in autonomous marine robots.

This paper presents a capacitive microphone sensor (CMS)-based multi-sensor fusion wearable system for intuitive and responsive control of mobile manipulators in smart home environments. The wearable integrates structurally optimized CMSs, inertial measurement units (IMUs), vibration motors, and pressure sensors to detect forearm muscle activity and arm motion in real time. To enhance signal fidelity, each CMS is embedded in a conical acoustic chamber filled with damping silicone, significantly improving robustness under noisy conditions. A CNN-based classification model is employed to interpret six combined gesture-force classes, achieving 93.89% offline accuracy. Real-time experiments involving five participants yielded 83.33% practical accuracy with an average system latency of 1.2 seconds. The wearable system enables real-time gesture recognition, adaptive force feedback, and precise robot control, validated through object manipulation tasks under various texture and weight conditions. These results highlight the feasibility and effectiveness of CMS-driven multi-sensor fusion werable device for low-latency, noise-resilient, and user-adaptive assistive robotics in complex indoor environments.

Inducing muscle activity with electric current - Functional Electrical Stimulation (FES) - is a key therapy for improving extremity function after neurological trauma (e.g. post-stroke). Positive outcomes, however, are heavily dependent on modulating stimulation to maximise response while limiting rapid FES-induced muscle fatigue. Feedback control is not possible today with standard electromyography (EMG); electrical stimulation obscures EMG measurement, severely limiting range of use and benefits. Mechanomyography (MMG), though underexplored, is immune to electrical artifacts, enabling closed-loop modulation. We introduce and validate a novel MMG-FES system to optimise therapeutic stimulation post-stroke. It combines a new wearable with two myography modalities (pressure P_MMG, microphone M_MMG), an MMG- Tibialis Anterior (TA) musculotendon model and an MMG-derived fatigue index. An isometric FES fatigue protocol is executed on control ($N=15$) and post-stroke ($N=3$) participants, recording force and MMG signals. The P_MMG Mean Value (MV) signals consistently decreased with fatigue, showing strong Pearson correlations ($\bar{r}$) with force decline in control ($\bar{r}=0.740$) and stroke ($\bar{r}=0.928$) groups ($p \le 0.005$). The P_MMG MV-driven model also accurately predicted force decline, achieving mean coefficients of determination ($R^{2}$) of 0.741 (control) and 0.774 (stroke), with strong prediction correlations ($\bar{r} > 0.87, p < 0.01$). Conversely, M_MMG prediction had weaker correlation to force and fatigue. We conclude: 1) the pressure-based P_MMG sensor is a robust, non-invasive FES-induced fatigue indicator; 2) the P_MMG-driven model allows continuous estimation of force capacity; and 3) the combined system enables closed-loop FES modulation to optimize rehabilitation.

In the realm of precision cattle health monitoring, this paper introduces the development and evaluation of a novel wearable continuous health monitoring device designed for cattle. The device integrates a sustainable solar-powered module, real-time signal acquisition and processing, and a storage module within an animal ergonomically designed curved casing for non-invasive cattle health monitoring. The curvature of the casing is tailored to better fit the contours of the cattle’s neck, significantly enhancing signal accuracy, particularly in temperature signal acquisition. The core module is equipped with precision temperature sensors and inertial measurement units, utilizing the Arduino MKR ZERO board for data acquisition and processing. Field tests conducted on a cohort of ten cattle not only validated the accuracy of temperature sensing but also demonstrated the potential of machine learning, particularly the Support Vector Machine algorithm, for precise behavior classification and step counting, with an average accuracy of 97.27%. This study innovatively combines real-time temperature recognition, behavior classification, and step counting organically within a self-powered device. The results underscore the feasibility of this technology in enhancing cattle welfare and farm management efficiency, providing clear direction for future research to further enhance these devices for large-scale applications.

Nocturnal mammals such as rats heavily depend on whisker based tactile perception to find their way through burrows and to identify objects. There is diversity in the whiskers in terms of the physical structure and nervous innervation. The robotics community has developed many different whisker sensors inspired by this biological basis. They take diverse mechanical, electronic, and computational approaches to use whiskers to identify the geometry, mechanical properties, and texture of objects. Some work address specific object features and some others address multiple features. Therefore, it is vital to have a comprehensive discussion of the literature and to understand the merits of both bio-inspired and pure engineered approaches to whisker based tactile perception. In this paper we report and discuss the progress in following areas: The body of mammalian whisker follicle, unimodal whiskered sensors, multimodal whiskered sensors with variable stiffness that can capture tactile sensory stimuli of different frequencies, obstacles detection, shape detection, texture classification and robot navigation using whiskers.

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.

My research objective is to explicitly bridge the gap between high computational performance and low power dissipation of robot on-board hardware by designing a bio-inspired tapered whisker neuromorphic computing (also called reservoir computing) system for offroad robot environment perception and navigation, that centres the interaction between a robot's body and its environment. Mobile robots performing tasks in unknown environments need to traverse a variety of complex terrains, and they must be able to reliably and quickly identify and characterize these terrains to avoid getting into potentially challenging or catastrophic circumstances. To solve this problem, I drew inspiration from animals like rats and seals, just relying on whiskers to perceive surroundings information and survive in dark and narrow environments. Additionally, I looked to the human cochlear which can separate different frequencies of sound. Based on these insights, my work addresses this need by exploring the physical whisker-based reservoir computing for quick and cost-efficient mobile robots environment perception and navigation step by step. This research could help us understand how the compliance of the biological counterparts helps robots to dynamically interact with the environment and provides a new solution compared with current methods for robot environment perception and navigation with limited computational resources, such as Mars.