Wearable Low-power Closed-loop System for Tremor Detection and Stimulation using Electromyography (EMG)

Abstract

A wearable EMG based tremor detection and suppression system is presented. This work proposes a novel design enabling low-power consumption, wearability, lower computational cost and lower latency. An analog front end (AFE) is designed containing cascaded filters and a Driven-Right-Leg (DRL) feedback for high-level noise removal of up to 1V. A CC1352R microcontroller with an integrated BLE along with RTOS is utilized to achieve low-power processing. A user-friendly interface is provided using Android application (AP) that allows immediate sharing of data to caretakers or database. A 128-point FFT is employed with a simple implementation in terms of computation and a variable-voltage skin-impedance based muscle stimulation is being used. The system is operable on coin cell batteries for more than 3 weeks. The overall average power consumption of the system is 4.8mW with average current 1.35mA and a detection latency of <0.2s is achieved.