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Open AccessArticle

A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies

Micrel Lab, Unversity of Bologna, 40126 Bologna, Italy
E3DA, Fondazione Bruno Kessler, 38123 Trento, Italy
Centro protesi INAIL, Vigorso di Budrio, 40054 Bologna, Italy
Integrated Systems Laboratory, ETHZ, 8092 Zurich, Switzerland
Authors to whom correspondence should be addressed.
Sensors 2017, 17(4), 869;
Received: 15 February 2017 / Revised: 11 April 2017 / Accepted: 12 April 2017 / Published: 15 April 2017
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI). Recent research efforts explore the use of myoelectric gesture recognition for innovative interaction solutions, however there persists a considerable gap between research evaluation and implementation into successful complete systems. In this paper, we present the design of a wearable prosthetic hand controller, based on intuitive gesture recognition and a custom control strategy. The wearable node directly actuates a poliarticulated hand and wirelessly interacts with a personal gateway (i.e., a smartphone) for the training and personalization of the recognition algorithm. Through the whole system development, we address the challenge of integrating an efficient embedded gesture classifier with a control strategy tailored for an intuitive interaction between the user and the prosthesis. We demonstrate that this combined approach outperforms systems based on mere pattern recognition, since they target the accuracy of a classification algorithm rather than the control of a gesture. The system was fully implemented, tested on healthy and amputee subjects and compared against benchmark repositories. The proposed approach achieves an error rate of 1.6% in the end-to-end real time control of commonly used hand gestures, while complying with the power and performance budget of a low-cost microcontroller. View Full-Text
Keywords: EMG; gesture recognition; prosthetics; BSN; human machine interaction EMG; gesture recognition; prosthetics; BSN; human machine interaction
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Benatti, S.; Milosevic, B.; Farella, E.; Gruppioni, E.; Benini, L. A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies. Sensors 2017, 17, 869.

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