Comparative analysis of force sensitive resistor circuitry for use in force myography systems for hand gesture recognition - Sagastume, Giancarlo K.; Young, Peyton R.; Battraw, Marcus A.; Kwong, Justin G.; Schofield, Jonathon S. (Frontiers in Electronics, 2024)
Wearable technologies for hand gesture classification are becoming increasingly prominent due to the growing need for more natural, human-centered control of complex devices. This need is particularly evident in emerging fields such as virtual reality and bionic prostheses, which require precise control with minimal delay. One method used for hand gesture recognition is force myography (FMG), which utilizes non-invasive pressure sensors to measure radial muscle forces on the skin’s surface of the forearm during hand movements. These sensors, typically force-sensitive resistors (FSRs), require additional circuitry to generate analog output signals, which are then classified using machine learning to derive corresponding control signals for the device. The performance of hand gesture classification can be influenced by the characteristics of this output signal, which may vary depending on the circuitry used. Our study examined three commonly used circuits in FMG systems: the voltage divider (VD), unity gain amplifier (UGA), and transimpedance amplifier (TIA). We first conducted benchtop testing of FSRs to characterize the impact of this circuitry on linearity, deadband, hysteresis, and drift, all metrics with the potential to influence an FMG system’s performance. To evaluate the circuit’s performance in hand gesture classification, we constructed an FMG band with 8 FSRs, using an adjustable Velcro strap and interchangeable circuitry. Wearing the FMG band, participants (N = 15) were instructed to perform 10 hand gestures commonly used in daily living. Our findings indicated that the UGA circuit outperformed others in minimizing hysteresis, drift and deadband with comparable results to the VD, while the TIA circuit excelled in ensuring linearity. Further, contemporary machine learning algorithms used to detect hand gestures were unaffected by the circuitry employed. These results suggest that applications of FMG requiring precise sensing of force values would likely benefit from use of the UGA. Alternatively, if hand gesture state classification is the only use case, developers can take advantage of benefits offered from using less complex circuitry such as the VD.