Application of Novel Graphite Flex Sensors in Closed-Loop Angle Feedback on a Soft Robotic Glove for Stroke Rehabilitation

Abstract

ABSTRACT Introduction Stroke survivors require physiotherapy and rehabilitation programs to restore their hand function so that they can carry out activities of daily living (ADLs) independently. Soft robotic gloves are designed to assist in these rehabilitation programs and reduce manpower costs, but their pressure-activated actuation mechanisms require closed-loop position feedback to allow for finer motor coordination for the hands, thereby improving hand rehabilitation for patients who had stroke. We present a novel design of graphite-based flex sensors that we implemented in a soft robotic glove to evaluate its performance in closed-loop metacarpophalangeal (MCP) joint angle feedback. Materials and Methods The graphite-based flex sensors are embedded into a sensor glove and characterized in terms of baseline stability and drift over 20 continuous loading cycles per trial for five times. Curve-fitting using both linear and nonlinear equations was done to determine the relationship between resistance and MCP joint angle, using Vicon MX motion capture system to obtain 3D coordinates and joint angles, as well as separate Arduino circuitry to obtain signal voltage samples. Pneumatic pressures are regulated using proportional-integral-derivative (PID) control, with a safety factor (SF) of 1.2. Two control algorithms were developed to make use of angular feedback to control set point pressures: 1) Intent Recognition Mode makes use of a single MCP angle threshold at 50° to activate a maximum output pressure was set at 100 kPa (83.33 kPa after SF); and 2) Fixed Interval Assist Mode makes use of different MCP joint angle values (30°, 45°, 60°, and 90°) to derive corresponding set point pressures set at 25, 50, 75, and 100 kPa (20.83, 41.67, 62.50, and 83.33 kPa after SF). Results Nonlinear equations consistently provided a reasonably better fit as compared with the linear equation fit. However, in this work, the linear MCP joint angle models are preferred as a calibration method, because nonlinear equations are hard to implement in control algorithms in practice. PID control for Intent Recognition activates and deactivates at approximately 18% and 95% of each full flexion-extension exercise cycle progression, respectively. For Fixed Interval Assist Mode, thumb MCP joint angle feedback is less repeatable compared with that of the other fingers in the same experiment, possibly because of the difficulty in placement of the sensor at the thumb MCP joint, close proximity to other sensors, and physiological crosstalk between the fingers. Conclusions This work has presented a novel integration and implementation of graphite-based flex sensors with a soft robotic glove for stroke rehabilitation. The relationship between the signal voltage and the MCP joint angle varies greatly with anatomical differences between each individual, and with sensor placement. However, based on the experimental results, a linear mapping calibration algorithm for the graphite-based flex sensors was implemented, which also complements its robustness for the potential application on stroke rehabilitation. The effectiveness of the calibration algorithm is also thus demonstrated via the Intent Recognition and Fixed Angle Assist control algorithms.