This paper presents a wireless multichannel surface electromyography (sEMG) sensor which features a custom 0.13μm CMOS mixed-signal system-on-chip (SoC) analog frontend circuit. The proposed sensor includes 10 sEMG recording channels with tunable bandwidth (BW) and analog-to-digital converter (ADC) resolution. The SoC includes 10x bioamplifiers, 10x 3 rd order ΔΣ MASH 1-1-1 ADC, and 10x on-chip decimation filters (DF). This SoC provides the sEMG samples data through a serial peripheral interface (SPI) bus to a microcontroller unit (MCU) that then transfers the data to a wireless transceiver. We report sEMG waveforms acquired using a custom multichannel electrode module, and a comparison with a commercial grade system. Results show that the proposed integrated wireless SoC-based system compares well with the commercial grade sEMG recording system. The sensor has an input-referred noise of 2.5 μVrms (BW of 10-500 Hz), an input-dynamic range of 6 mVpp, a programmable sampling rate of 2 ksps, for sEMG, while consuming only 7.1 μW/Ch for the SoC (w/ ADC & DF) and 21.8 mW of power for the sensor (Transceiver, MCU, etc.). The system lies on a 1.5 × 2.0 cm 2 printed circuit board and weights <; 1 g

Bielmann, Mathieu

Bouyer, Laurent

Fall, Cheikh Latyr

Gagnon-Turcotte, Gabriel

Gosselin, Benoit

Mascret, Quentin

English

CorpusUL

1A Multichannel Wireless sEMG Sensor Endowing a0.13 μm CMOS Mixed-Signal SoCG. Gagnon-Turcotte, Student Member, IEEE, C. L. Fall, Student Member, IEEE, Q. Mascret, M. Bielmann, Student Member, IEEE, L. Bouyer and B. Gosselin, Member, IEEEG. Gagnon-Turcotte, C. L. Fall, Q. Mascret and B. Gosselin are with the Smart Biomedical Microsystems Lab., Université Laval, Quebec City, QC G1V 0A6, Canada. M. Bielmann and L. Bouyer are with the Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS), Quebec City, QC G1M 2S8, Canada.AbstractThis paper presents a wireless multichannel surface electromyography (sEMG) sensor which features a custom 0.13-µm CMOS mixed-signal system-on-chip (SoC) analog front-end circuit. The proposed sensor includes 10 sEMG recording channels with tunable bandwidth (BW) and analog-to-digital converter (ADC) resolution. The SoC includes 10x bioamplifiers, 10 x 3rd order ∆Σ MASH 1-1-1 ADC, and 10x on-chip decimation filters (DF). This SoC provides the sEMG samples data through a serial peripheral interface (SPI) bus to a microcontroller unit (MCU) that then transfers the data to a wireless transceiver. We report sEMG waveforms acquired using a custom multichannel electrode module, and a comparison with a commercial grade system. Results show that the proposed integrated wireless SoC-based system compares well with the commercial grade sEMG recording system. The sensor has an input-referred noise of 2.5 µVrms (BW of 10-500 Hz), an input-dynamic range of 6 mVpp, a programmable sampling rate of 2 ksps, for sEMG, while consuming only 7.1 µW/Ch for the SoC (w/ ADC & DF) and 21.8 mW of power for the sensor (Transceiver, MCU, etc.). The system lies on a 1.5 x 2.0 cm2 printed circuit board and weights < 1 g.Index Terms—sEMG recording; system-on-chip; wearable; low-power; wireless body sensor; multichannel;I. INTRODUCTIONSINCE the advent of electrophysiology theories in the early 1800’s [1] and the development of measurement tools such as the Einthoven’s string galvanometer in the early 1900’s, the interest for surface electromyography (sEMG) has been growing considerably over the last century. The muscles electrical activity contains a large amount of information (tone, motor unit recruitment profile, etc.) that can be used in a wide range of applications (e.g. rehabilitation, human-machine interactions, etc.) [2]. To support the growing popularity of sEMG applications that are now moving outside of the classical laboratory setting, measurement systems are required to allow the simultaneous recording of multiple channels, be small and comfortable (wireless, size, etc.), have low power consumption, and be precise and sensitive with respect to the sEMG signal’s useful frequency bandwidth (BW) and amplitude, immune to noise (e.g. interference, motion artifacts) and safe of use [3]. Commercially available solutions often lack in providing the right trade-off between these characteristics.Wireless sEMG sensor devices such as the Myo Armband from Thalmic Labs provides a suitable solution used in a wide range of applications. Although it allows for a simultaneous 8-channel recording, the frequency BW it provides is limited to 200Hz, thereby omitting an important part of the human EMG spectral content located between 200 and 450Hz [4]. The Trigno™ sEMG nodes from Delsys overcome this limitation by providing a modular sensor network, but only integrates a single channel per node [5]. The Quattro from OT Bioelettronica implements up to 4 channels within a single device, 59 x 95 mm2, making it hard to carry during experiments [6]. When it comes to designing custom sEMG measurement tools, system-on-chip (SoC) bioamplifier circuits provide integrated multichannel solutions that may be the best currently available solution. For example, Intan Technologies amplifier chips such as the RHD2164 are suitable for highdensity sEMG (providing up to 64 recording channels), and the ADS129X chips from Texas Instrument provides up to 8 channels consuming 750 μW/Ch, for theoretical input referred noise (IRN) levels of 2.4 mVrms and 6 μVrms [7], [8].The work presented in this paper is part of an ongoing project aiming to provide a multichannel, small form factor wearable and wireless sEMG sensing solution to support performance tracking and rehabilitation applications. We present a 10-channels wireless sEMG sensor that is based on a custom 0.13 μm CMOS mixed-signal SoC. Compared to existing systems, the proposed sensor is small [8]–[10], has a high channel count [6], [8]–[11], while having better or equivalent noise, power [7], [8], [11] and sampling rate [9] performances.II. SYSTEM OVERVIEW AND DESIGNFigure 1 shows the system-level concept of the proposed multichannel wireless sEMG sensor. The complete system consists of two main interconnected parts. The first part is the sensor printed circuit board (PCB) that includes the electronics components for signal amplification, filtering, data acquisition and wireless transmission, while the second part is the detachable module holding the sEMG electrodes. The proposed sensor consists of four main building blocks: 1) the custom 0.13 μm mixed-signal SoC conditioning and digitizing 10 sEMG channels in parallel, 2) a low-power MSP430F5328 microcontroller unit (MCU) from Texas Instruments, USA, an MCU family used in many low-power biomedical monitoring applications [12], [13] available in a tiny 5 x 5 mm2 micro BGA package, 3) a 2.4-GHz nRF24L01+ wireless transceiver from Nordic Semiconductors, Norway, 4) a power management unit consisting two low-dropout regulators (LDO), providing a supply voltage of 1.9-V for the MCU and the wireless transceiver, and a supply voltage of 1.2-V for the SoC.2A. Custom Mixed-Signal SoC DesignThe acquisition of the low-amplitude EMG signals from up to 10 electrodes in parallel is performed by a custom SoC [12] fabricated in the GF (IBM) 0.13 μm CMOS process. This dedicated SoC provides low input-referred noise (IRN), lowpower consumption, a high channel count, a tunable BW, a tunable analog-to-digital converter (ADC) resolution, a serial peripheral interface (SPI), within a small chip size of 2.0 mm2. As seen in Figure 1, the bioamplifier consist of a fully-differential single-stage AC-coupled operational transconductance amplifier (OTA) having a closed-loop gain of 46 dB [12]. In this topology, the feedback resistors are implemented using a 3-bits tunable pseudo-resistors bank to achieve various high-pass cutoff frequency (either ~20.5 Hz, ~210 Hz, ~2200 Hz or ~2 400 Hz), as to accommodate different type of biological signals (sEMG, action potentials, local field potentials, etc.), and the analog low-pass cutoff frequency is fixed at 7 kHz by the Gm-C filter created by the OTA and an output capacitor [12]. It should be noted that tunable low-pass filtering is provided digitally, as described in the following paragraph. In the proposed topology, each bioamplifier has differential inputs, with one shared reference between all the channels. Thus, the bioamplifier amplifies the signal between the channel (1-10) and the shared electrodes. The body reference is generated with an on-chip LDO (VDDSoC/2 = 0.6-V), the potential of which can be applied to a dedicated electrode, or connected to the shared electrode. In the proposed sensor, the latter strategy is used to reduce the number of electrodes to a minimum (11 electrodes are required to record 10 sEMG channels, versus 21 for a classical differential recording system [5], [8], [11]).As seen in Figure 1, the ADC consists of a 3rd order fully-differential ΔΣ MASH 1-1-1 followed by an on-chip 4th-order cascaded integrator-comb (CIC4) decimation filter (DF) [12], while typical systems use off-chip DF [11]. The oversampling ratio (OSR) and the sampling frequency can be adjusted to achieve the required resolution and BW (up to an OSR of 50). Indeed, the DF allows to set the digital low-pass cutoff frequency by varying the OSR and/or the oversampling frequency (fov) [12]. Thus, by using an OSR of 25 and fov = 50 kHz (fs = 2 kHz after decimation), a low-pass digital cutoff frequency of 500 Hz is achieved, which is suitable for sEMG recordings [11]. It is worth noting that the low-pass analog filtering at 7-kHz does not produce any aliasing, even with a sampling rate of 2-ksps, as the Nyquist frequency is pushed to 25 kHz by the ΔΣ oversampling.In addition to providing the aforementioned features, the proposed SoC communicates through a SPI bus, allowing to reconfigure the high-pass filters and the DF on-the-fly, and to retrieve the ADC samples. The SPI bus is designed to work at frequencies up to 20 MHz, while it is possible to increase the resolution by putting multiple SoC in parallel, all connected on the same SPI bus. B. Mixed-Signal Control System & Wireless TransceiverCommunication with the SoC and with the nRF24L01+ are carried out by a MSP430F5328 MCU from Texas Instrument. To optimize the use of MCU resources, the direct-memory access (DMA) controller is used to retrieve the ADC samples from the SoC at maximum speed, and to redirect them to the wireless transceiver. A 20-kHz timer interrupt is used to trigger a DMA transfer (20kHz/10ch = 2 kHz/ch) of the read command and the channel number to be read. The SoC responds by putting the MSB and LSB bytes on the SPI bus, which are further redirected to nRF24L01+ by another DMA channel. The MCU generates the clock signal used by the ΔΣ, simply by producing a PWM signal with a duty-cycle of 50%.C. Full SystemFigure 2 shows the multichannel wireless sEMG sensor PCB with all the components assembled. The SoC is wirebonded directly on the PCB (w/ 25 μm gold wires), as to save weight and size. A drop of epoxy is filled on top of the SoC to protect the bondings and the die when the PCB is manipulated. To improve the recording quality, a ferrite bead isolates the analog and digital grounds. The system connects to the electrode module using a Molex SlimStack connector.III. EXPERIMENTAL RESULTSA. Multichannel Surface EMG RecordingsWe performed multichannel sEMG recordings using the custom electrode module depicted in Figure 3a (left). This module consists of 5 PCBs, each of them holding one reference and two recording copper electrodes (in pair: 10-9, 8-7, 6-5, 4-3 and 2-1). The reference electrodes are interconnected from one PCB to another, while the recording electrodes are braided around the reference and connected to the main PCB that is connected to the sEMG sensor. Figure 3a (right) shows the electrode placement around the forearm, placed to cover two muscles, the extensor carpi and the flexor carpi. The extensor carpi was covered by the electrodes 1-2 and the flexor carpi was covered by the electrodes 7-10, while the electrodes 3-6 were located between them.Figure 3b (bottom) shows 55 seconds of multichannel recordings while performing four distinct movements shown in Figure 3b (top). Raising the hand up was soliciting the extensor carpi muscle, which generated among the strongest sEMG signals recorded by the electrodes 1-6 (2-4 mVpp amplitude). Contrastingly, lowering the hand down, which was soliciting the flexor carpi muscle, did not generate any significant sEMG activity on these electrodes. However, the sEMG generated by this movement is clearly visible on the electrodes 7-10 (3-4 mVpp amplitude). Turning the wrist to the right generated a similar sEMG activity on all the electrodes (both flexor and extensor carpi muscle activated). The same phenomenon occurred when clenching the fist, but with a higher amplitude on most electrodes. This simple experiment demonstrates that the multichannel capability of the proposed sensor can allow to discriminate the muscular activity of many muscles at the same time.3B. Comparison w/ NTI sEMG SensorWe compared the proposed sensor with a commercially available, laboratory grade sEMG system (NTI, Neogenix Technologies, Quebec City, Canada). For this experiment, both systems were connected, one after the other, to the same Ag/AgCl electrodes that were placed on the extensor carpi muscle (inter-electrode distance of ~2 cm). The surface electromyography (sEMG) signal was recorded during three consecutive isometric maximal voluntary contractions (MVC; 5s/contraction) and during a relaxation test (10s). MVC is a common method used to recruit a large proportion of a muscle’s motor units. This test therefore provides a simple mean to measure the ability of the new systems to record a large number of muscle action potentials. As a muscle is composed of hundreds of motor units firing at different rates (from slow fatigue-resistant to fast fatigue-sensitive units), comparing the amplitude and power spectrum of the signal recorded by both systems allows to quantify the system’s performance within a very short period of time. Analysis was performed on each contraction burst. Figure 4 shows that amplitude of the EMG signals were very closed with an average noise level similar or better on the proposed system. Similarly, Figure 5 shows that the proposed system power spectrum has a frequency content similar to the NTI system, with slightly larger output at high frequencies (150-200 Hz). C. Overall PerformancesThe measured system characteristics are summarized in Table I. In summary, the dynamic range, IRN, sampling rate and effective number of bits (ENOB) are of 6 mVpp, 2.5 μVrms (10-500 Hz BW), 2 kbps and 9.75-bits (at an OSR of 25), respectively, while the system is small (1.5 x 2.0 cm2) and lightweight (< 1 g, w/o battery). Moreover, the BW and the sampling rate can be tuned to cover a wide range of biological signals from 0.5 Hz to 7 kHz. The power consumption breakdown of the different building blocks is presented in Figure 6. The power consumption of the SoC is of 71 μW for the 10 channels, the transceiver consumes 7.19 mW (effective data-rate of 320 kbps at 1 Mbps on-air) and the MCU consumes 14.32 mW at 16 MHz. The total power consumption of the whole sensor is 21.8 mW.IV. CONCLUSIONWe presented a new miniature, wireless multichannel sEMG sensor suitable to record up to 10 channels simultaneously with a 2-kHz sampling rate. The proposed system is based on a custom 0.13 μm CMOS mixed-signal SoC for signal acquisition, that allows increasing the number of recorded channels, reducing power consumption, size, and weight of the whole sensor. The proposed system has been validated for multichannel recordings using a custom electrode module, and also compared to a commercial system. The experimental results demonstrate that the proposed system has better or equivalent characteristics (IRN, BW, power consumption, sampling rate, etc.) than the other available wireless systems, while being smaller and lighter.REFERENCES[1] F. Isch, “Histoire de l’electromyographie: Le temps des pionniers”, Revue d’Electroencéphalographie et de Neurophysiologie Clinique, vol. 17, pp. 1s-8s, 1987, Elseiver.[2] R. Merletti and D. Farina, “Surface electromyography: physiology, engineering and applications”, John Wiley & Sons, 2016.[3] B. Milosevic, S. Benatti and E. Farella, “Design challenges for wearable EMG applications”, IEEE DATE’17, pp. 1432-1437, 2017.[4] U. Côté-Allard et al., “Transfer learning for sEMG hand gestures recognition using convolutional neural networks”, IEEE SMC’17, pp. 1663-1668, 2017.[5] Delsys Wireless sEMG. Accessed: Sep. 11, 2018 [Online]: https://www.delsys.com/products/wireless-emg/[6] Quattro sEMG Platform. Accessed: Sep. 11, 2018 [Online]: http://www.otbioelettronica.it/[7] K. Fujikawa et et al., “Estimation of fingertip forces using high-density surface electromyography”, IEEE MHS’17, pp. 1-5, 2017.[8] C. L. Fall et al., “A multimodal adaptive wireless control interface for people with upper-body disabilities”, IEEE Trans. Biomed. Circuits Syst., vol. 12, no. 3, pp. 564-575, 2018.[9] Myo Gesture Control Armband. Accessed: Sep. 11, 2018 [Online]: https://www.myo.com/[10] S.-C. Lee et al., “A wireless multi-channel physiological signal acquisition system-on-chip for wearable devices”, IEEE SENSORS, 2016.[11] B.-S. Chung et al., “Analog Front-End for EMG Acquisition System”, IEEE ISOCC’17, 2017.[12] G. Gagnon-Turcotte et al., “A 13μm CMOS SoC for simultaneous multichannel optogenetics and electrophysiological brain recording”, IEEE ISSCC’18, pp. 466-468, 2018.[13] H. Park et al., “A Wireless Magnetoresistive Sensing System for an Intraoral Tongue-Computer Interface”, IEEE Trans. Biomed. Circuits Syst., vol. 6, no. 6, pp. 571-585, 2012.45678910

A multichannel wireless sEMG sensor endowing a 0.13 μm CMOS mixed-signal SoC

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A Multichannel Wireless sEMG Sensor Endowing a <tex>$0.13 \ \mu \mathrm{m}$</tex> CMOS Mixed-Signal SoC

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A multichannel wireless sEMG sensor endowing a 0.13 μm CMOS mixed-signal SoC

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