title: A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.
abstract: Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.
authors.forename: Simone
authors.initials: S
authors.lastname: Benatti
authors.forename: Filippo
authors.initials: F
authors.lastname: Casamassima
authors.forename: Bojan
authors.initials: B
authors.lastname: Milosevic
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Farella
authors.forename: Philipp
authors.initials: P
authors.lastname: Schönle
authors.forename: Schekeb
authors.initials: S
authors.lastname: Fateh
authors.forename: Thomas
authors.initials: T
authors.lastname: Burger
authors.forename: Qiuting
authors.initials: Q
authors.lastname: Huang
authors.forename: Luca
authors.initials: L
authors.lastname: Benini
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering / Q000295:instrumentation; D003020:Clothing; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D005542:Forearm / Q000502:physiology; D005868:Gestures; D006801:Humans; D018482:Muscle, Skeletal / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results