A Prosthetic Limb Managed by Sensors-Based Electronic System: Experimental Results on Amputees
| Autor: | F. Gaetani, G. A. Zappatore, R. de Fazio, Paolo Visconti |
|---|---|
| Přispěvatelé: | Gaetani, F., De Fazio, R., Zappatore, G. A., Visconti, P. |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer Networks and Communications Computer science System testing Cloud computing 02 engineering and technology Myoelectric signals 01 natural sciences law.invention 020901 industrial engineering & automation Touchscreen Software Prosthetic limb law Inertial measurement unit Arduino Sensors and electronic boards Computer Science (miscellaneous) Gestures recognition algorithm Myoelectric signals Prosthetic limb Sensors and electronic boards Signals acquisition and processing System testing Wireless connectivity Electrical and Electronic Engineering Instrumentation Signals acquisition and processing Simulation business.industry 010401 analytical chemistry Wireless connectivity 0104 chemical sciences Hardware and Architecture Control and Systems Engineering Transceiver business Information Systems Gesture Gestures recognition algorithm |
| Popis: | Taking the advantages offered by smart high-performance electronic devices, transradial prosthesis for upper-limb amputees was developed and tested. It is equipped with sensing devices and actuators allowing hand movements; myoelectric signals are detected by Myo armband with 8 ElectroMyoGraphic (EMG) electrodes, a 9-axis Inertial Measurement Unit (IMU) and Bluetooth Low Energy (BLE) module. All data are received through HM-11 BLE transceiver by Arduino board which processes them and drives actuators. Raspberry Pi board controls a touchscreen display, providing user a feedback related to prosthesis functioning and sends EMG and IMU data, gathered via the armband, to cloud platform thus allowing orthopedic during rehabilitation period, to monitor users’ improvements in real time. A GUI software integrating a machine learning algorithm was implemented for recognizing flexion/extension/rest gestures of user fingers. The algorithm performances were tested on 9 male subjects (8 able-bodied and 1 subject affected by upper-limb amelia), demonstrating high accuracy and fast responses. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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