Development of a Wireless Robotic Arm Control System Using Piezoelectric Sensors and Neural Networks
A robotic arm capable of mimicking hand gestures through data analysis of the mechanical activity of the muscles, using wearable piezoelectric sensors was developed. Piezoelectric discs were placed in the key locations of the forearm and gestures from the arm were predicted by the system using an approximator neural network. Each approximator creates a characteristic curve and event data for each sensor correlating to each finger movement. Using a segmentator, the event can be recorded, then compared to the approximated data by calculating the root mean square error (RMSE) to find the most similar, identifying the gesture. After classification, the result is transmitted wirelessly using Bluetooth low energy (BLE) to a robotic hand to mimic the gesture.
Published information regarding this project:
J. I. Rodriguez-Labra, B. B. Narakathu and M. Z. Atashbar, “Development of a Wireless Robotic Arm Control System Using Piezoelectric Sensors and Neural Networks,” 2019 IEEE SENSORS, Montreal, QC, Canada, 2019, pp. 1-4, doi: 10.1109/SENSORS43011.2019.8956669.
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