Problem
Research questions and friction points this paper is trying to address.
closed-loop sensorimotor training
reconfigurable platform
on-body sensing
vibrotactile feedback
modular system
Sensorimotor Stickies: A Reconfigurable On-Body Platform for Closed-Loop Sensorimotor Training
🤖 AI Summary
This work addresses the limited reusability of existing closed-loop sensorimotor training systems, which are typically tailored to specific tasks and lack cross-scenario adaptability. The authors propose a modular, reconfigurable, skin-mounted platform that integrates inertial sensing, tactile perception, and vibrotactile feedback into miniature adhesive “patches.” A unified mobile application enables hardware configuration, calibration, and customizable feedback logic, while the interface is designed around a body-centric model. Leveraging low-power firmware and BLE communication, the system supports diverse training protocols and user requirements. Experimental validation demonstrates the platform’s technical feasibility, high reconfigurability, and capacity to allow first-time users to autonomously set up the system, thereby significantly enhancing the generality and flexibility of sensorimotor training interventions.
📝 Abstract
Closed-loop sensorimotor training systems can improve learning by sensing movement and delivering real-time feedback, yet most are built as fixed implementations tied to a single task, even though the core technology (inertial and tactile sensing, vibrotactile cueing, rule-based logic) remains the same. We present Sensorimotor Stickies, a reconfigurable on-body platform that treats sensing and vibrotactile feedback as modular stickies that can be patched onto the body as needed. The platform includes miniaturized adhesive modules for IMU sensing, optional tactile sensing, and vibrotactile actuation; low-power firmware and BLE infrastructure for raw streaming and motor control without task-specific rewrites; and a companion mobile app that provides a shared body-centered model for placement, calibration, and feedback authoring. Together, these components enable reconfiguration across training scenarios, user needs, and feedback setups. We evaluate the platform through technical characterization, configured application demonstration, practitioner-mediated configuration sessions, and an end-user study, demonstrating technical feasibility, reconfiguration breadth, and end-user configurability for first-time setup, calibration, and within-task feedback reconfiguration.
Innovation
Methods, ideas, or system contributions that make the work stand out.
reconfigurable platform
modular sensorimotor feedback
on-body interface
vibrotactile cueing
closed-loop training
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