At the Rehabilitation, Assistive Tech & Human Control Theory (REACT) Laboratory, I work under the guidance of Dr. Brendan Smith on cutting-edge research in assistive technology. My focus is on measuring and analyzing the grip force control of children with cerebral palsy using load cell technology to inform the design of robotic hand exoskeletons. To enhance data collection efficiency, I am developing a state machine-based game using Python and C++ on Raspberry Pi 5 and Adafruit Feather M4, improving data speeds by over 25%.
I am amplifying the voltage difference captured in the load cell using an INA 125 to make the voltage readable to the Adafruit Feather M4. The resistor at the top sets the gain, therefore setting the voltage range possible. In this example, I am using a 10k Ω resistor.