In recent years, the application of IoT for health purposes, including the intense use of wearable devices, has been considerably growing. Among the wearable devices, the systems for measuring EMG (electromyography) signals are highly investigated. The possibility of recording different signals in a multichannel approach can lead to reliable data that can be used to improve diagnostic techniques, analyze performance in sports professionals and perform remote rehabilitation. In this work, we describe the design of a novel wearable system for surface EMG using a compact electronic board and a printed matrix of electrodes. The whole system has an estimated maximum current absorption of 55 mA at 3.3 V. We focused on the subsystem integration and on the real-time data transmission through Bluetooth Low Energy (BLE) with a throughput of 28 kB/s with a success rate of 99%. Some preliminary data are collected on a healthy man’s arm to validate the design. The acquired data are then analyzed and processed to improve information quality and extract contraction patterns.

Novel Wearable System for Surface EMG Using Compact Electronic Board and Printed Matrix of Electrodes

Lopomo N. F.;
2021-01-01

Abstract

In recent years, the application of IoT for health purposes, including the intense use of wearable devices, has been considerably growing. Among the wearable devices, the systems for measuring EMG (electromyography) signals are highly investigated. The possibility of recording different signals in a multichannel approach can lead to reliable data that can be used to improve diagnostic techniques, analyze performance in sports professionals and perform remote rehabilitation. In this work, we describe the design of a novel wearable system for surface EMG using a compact electronic board and a printed matrix of electrodes. The whole system has an estimated maximum current absorption of 55 mA at 3.3 V. We focused on the subsystem integration and on the real-time data transmission through Bluetooth Low Energy (BLE) with a throughput of 28 kB/s with a success rate of 99%. Some preliminary data are collected on a healthy man’s arm to validate the design. The acquired data are then analyzed and processed to improve information quality and extract contraction patterns.
2021
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
978-3-030-69962-8
Multielectrode EMG
Printed electrodes
Wearable device
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1264185
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