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.

A Prosthetic Limb Managed by Sensors-Based Electronic System: Experimental Results on Amputees

R. De Fazio
Writing – Original Draft Preparation
;
P. Visconti
Writing – Review & Editing
2020-01-01

Abstract

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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/434554
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 14
  • ???jsp.display-item.citation.isi??? ND
social impact