Flexible, Real-Time Sensorized Grooved Pegboard Test Platform for Closed-Loop Neuromodulation
Year: 2025
Type: conference-paper
Abstract: Fine motor deficits significantly impact individuals with Multiple Sclerosis (MS), and current therapies often fall short in promoting functional recovery. Paired Vagus Nerve Stimulation (VNS) with motor rehabilitation presents a promising therapeutic avenue, but its application requires precise real-time monitoring of task performance. This paper details the development and validation of a novel closed-loop sensorized grooved pegboard test (GPT) system designed to enable accurately timed VNS. The system utilizes a scale-based sensor integrated with an ESP32 microcontroller for rapid peg placement detection and a Raspberry Pi for overall system control, data logging, and graphical user interface. Validation studies involving healthy individuals ($n=7$) demonstrated the high detection accuracy of the system, achieving a true positive rate of 98.9 % and a false positive rate of 0.9 % (based on the aggregate confusion matrix analysis). Furthermore, the system successfully captured expected behavioral metrics, including learning effects and interhand performance differences during GPT execution. This validated, sensorized GPT provides a reliable and potentially home-deployable platform for investigating paired-VNS as a therapeutic strategy to aid in the recovery of fine motor impairments in MS, facilitating the crucial translation of promising preclinical research into clinical applications.
Authors: Shayok Dutta, Adrienne Ellett, Cristin Welle
Institution: University of Colorado Anschutz
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7167841973