Functional validation and improvement of a low-cost control system for assistive robotic devices

The goal of this research is to increase the affordability of portable and intuitive control systems for assistive robotic devices used by patients and researchers. Current control systems are either expensive or utilize unintuitive inputs such as foot-mounted inertia measurement units (for prostheses) or oral sipand- puff devices (for wheelchairs). These unintuitive control strategies place unnecessary physical and mental strain on users and lead to the abandonment of assistive devices. Here we present a low-cost intuitive control system based on surface electromyography (sEMG). The low-cost control system consists of an sEMG acquisition system and a single board computer. The high-performance single-board computer enables real-time machine learning to determine an individual's intended movements from sEMG. We integrated the low-cost control system with a commercially available prosthesis (i.e., DEKA "LUKE" Arm) and then validated the performance of the low-cost control system against a high-end research-grade control system (Ripple Nomad). We found no significant differences in functional hand dexterity between individuals using the low-cost and researchgrade control systems. The low-cost control system presented here can provide researchers and patients an inexpensive and portable option for more intuitive realtime control of robotic devices.