Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

Abstract

Device wearability and operating time are trending topics in recent state-of-art works on surface ElectroMyoGraphic (sEMG) muscle monitoring. No optimal trade-off, able to concurrently address several problems of the acquisition system like robustness, miniaturization, versatility, and power efficiency, has yet been found. In this tutorial we present a solution to most of these issues, embedding in a single device both an sEMG acquisition channel, with our custom event-driven hardware feature extraction technique (named Average Threshold Crossing), and a digital part, which includes a microcontroller unit, for (optionally) sEMG sampling and processing, and a Bluetooth communication, for wireless data transmission. The knowledge acquired by the research group brought to an accurate selection of each single component, resulting in a very efficient prototype, with a comfortable final size (57.8 mm x 25.2 mm x 22.1 mm) and a consistent signal-to-noise ratio of the acquired sEMG (higher than 15 dB). Furthermore, a precise design of the firmware has been performed, handling both signal acquisition and Bluetooth transmission concurrently, thanks to a FreeRTOS custom implementation. In particular, the system adapts to both sEMG and ATC transmission, with an application throughput up to 2 kB s −1 and an average operating time of 80 h (for high resolution sEMG sampling), relaxable to 8 B s −1 throughput and about 230 h operating time (considering a 110 mAh battery), in case of ATC acquisition only. Here we share our experience over the years in designing wearable systems for the sEMG detection, specifying in detail how our event-driven approach could benefit the device development phases. Some previous basic knowledge about biosignal acquisition, electronic circuits and programming would certainly ease the repeatability of this tutorial.

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

“…Data communication among the armband's boards is achieved by arranging the units in an I 2 C closed-loop daisy-chain configuration, whose API implements a custom protocol. As already anticipated in Section I, all the modules are based on our custom sEMG acquisition channel (detailed in [43]). Being equipped with an adaptable AFE for the TC signal, a low-power MCU for embedded ATC computations, and a Bluetooth Low Energy (BLE) [47] transceiver for wireless connectivity, the acquisition channel represents our wearable edge-computing node.…”

“…Being equipped with an adaptable AFE for the TC signal, a low-power MCU for embedded ATC computations, and a Bluetooth Low Energy (BLE) [47] transceiver for wireless connectivity, the acquisition channel represents our wearable edge-computing node. Evolving from [43], this second version of our prototype features more selectable AFE gains w.r.t. its predecessor, as the unique difference relevant to this project.…”

“…In [43] and [38] each board worked as a standalone module, streaming the ATC data independently. In this work, we maintained the same exact PCB design, fabrication and assembly to ensure the interchangeability of each board (e.g., in case of hardware faults) and to allow us to distribute the computational effort among the boards, if needed.…”

“…Contrary to our previous applications [39]- [41], which were all based on an old hardware version of our acquisition device [42], the armband we designed in this work is equipped with seven identical custom Printed Circuit Boards (PCBs), which are an upgraded version of the board described in [43] and already integrated in our last live demonstration [38]. These acquisition devices feature a digital part and an Analog Front-End (AFE) circuit for sEMG acquisition each, both designed with off-the-shelf components only.…”

“…To summarize, taking advantage of our previous research on the bio-inspired field [38], [40], [43], in this work we introduced the following novel contributions:…”