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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.

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The analysis of the surface ElectroMyoGraphic (sEMG) signal for controlling the Functional Electrical Stimulation (FES) therapy is being widely accepted as an active rehabilitation technique for the restoration of neuro-muscular disorders. Portability and real-time functionalities are major concerns, and, among others, two correlated challenges are the development of an embedded system and the implementation of lightweight signal processing approaches. In this respect, the event-driven nature of the Average Threshold Crossing (ATC) technique, considering its high correlation with the muscle force and the sparsity of its representation, could be an optimal solution. In this paper we present an embedded ATC-FES control system equipped with a multi-platform software featuring an easy-to-use Graphical User Interface (GUI). The system has been first characterized and validated by analyzing CPU and memory usage in different operating conditions, as well as measuring the system latency (fulfilling the real-time requirements with a 140 ms FES definition process). We also confirmed system effectiveness, testing it on 11 healthy subjects: The similarity between the voluntary movement and the stimulate one has been evaluated, computing the cross-correlation coefficient between the angular signals acquired during the limbs motion. We obtained high correlation values of 0.87 ± 0.07 and 0.93 ± 0.02 for the elbow flexion and knee extension exercises, respectively, proving good stimulation application in real therapy-scenarios.Sensors 2020, 20, 1535 2 of 22 and safety of the stimulation and the resulting performance, i.e., a latency short enough to fulfill the real-time constraints and the quality of the stimulated movement.We propose an embedded bio-mimetic FES system based on the Average Threshold Crossing (ATC) event-driven technique applied to the sEMG signal. The ATC essentially compares the sEMG signal with a threshold [6]: the Threshold Crossing (TC) events generate the quasi-digital TC signal, which is characterized by a digital waveform carrying analog (time-based) information. The ATC parameter is then computed by counting the number of TC events during a time window. In [7], we have demonstrated the correlation among ATC, ARV and the muscle force: in particular, having 0.95 ± 0.02 ATC-force w.r.t. 0.97 ± 0.02 ARV-force correlation, the ATC parameter can be used as indicator of muscle activity [8]. In this way, the event-driven approach enables the implementation of a low-complexity on-board feature extraction process, divided into two steps (TC generation and ATC computing), which can be directly performed in hardware [9,10], supporting, e.g., the recognition of different gestures [11][12][13][14]. While the theoretical background of ATC is quite similar to others common sEMG features, e.g., Zero-Crossing (ZC) or Wilson Amplitude (WAMP) [15], our event-based approach could overcome signal processing limitations for embedded feature extraction [16]. In particular, ZC and WAMP calculations are achieved by analyzing an a...

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