A Multisensor Wearable Device Based on Capacitive Microphone Sensors for Robust Human–Robot Interaction in Smart Home Environments - Jin,X; Xiao,B; Wang,H; Wang,W; Childs,P; Yu,Z (IEEE Sensors Journal, 2026)
This article presents a capacitive microphone sensor (CMS)-based multisensor 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 convolutional neural network (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 s. 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 a CMS-driven multisensor fusion wearable device for low-latency, noise-resilient, and user-adaptive assistive robotics in complex indoor environments.