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Embedded Wearable Integrating Real-Time Processing of Electromyography Signals

ABSTRACT

We realized a non-invasive wearable device able to record muscle activity using patch electrodes positioned on the skin over the muscle. It is an integrated system-on-board developed for the detection of several physical and physiologic human parameters which includes specific circuits for detecting the surface electromyography signal and algorithms for the real-time data processing optimized to low computational load.

In real time, the proposed system dissipates only 26 mW and guarantees 20 h battery autonomy. The system exhibits performance comparable with those achieved with state-of-art wired equipment used in the hospitals, but with the ad vantage of being an embedded wearable wireless device.

THE SYSTEM

Figure 1. Sketch of a typical raw EMG signal recorded with fine wires or patch electrodes

Figure 1. Sketch of a typical raw EMG signal recorded with fine wires or patch electrodes

To get reliable clinical and physiological information from neuromuscular functionality, the sEMG needs to detect the exact timing and duration of the muscle activities. The typical raw sEMG signal is sketched in Figure 1. Regardless of the type of physical connection used (fine wires or patch electrodes), the frequency components are typically in the band between 3 and 500 Hz with a main content between 10 and 250 Hz. In the case of the sEMG amplitude is typically between 0.1 and 2.0 mV (which rises up to 5.0 mV in very special cases as for athletes).

Figure 2. Sketch of the Bio2Bit Move: (a) the electrodes ; (b) case of use ; (c) blocks for sEMG signal acquisition

Figure 2. Sketch of the Bio2Bit Move: (a) the electrodes ; (b) case of use ; (c) blocks for sEMG signal acquisition

The starting point of the hard device was an integrated system-on-board (Bio2Bit Move) developed for the detection of several physical and physiologic human parameters (shown in Figure 2). The Bio2Bit Move contains an ultra-low power active front end for the acquisition of EMG signal, an ultra-low power MP ARM Cortex M4 at 32 bit, a Bluetooth Low Energy, a 592 mWh battery, a micro-USB connector. In general, the blocks of a circuit for sEMG signal acquisition are displayed in Figure 2c.

CONCLUSION

In conclusion, the proposed wearable device is the first one embedding the sEMG data processing and performing continuous non-invasive monitoring of the muscle activity with high accuracy and long-time battery autonomy. It exhibits sensitivity and specificity in the detection of muscle activity comp arable with those achieved with state-of-art wired equipment conventionally used in the hospitals, but with the advantage of being wireless and comfortably wearable.

Source: Sapienza University
Authors: Paolo Gentile | Marco Pessione | Antonio Suppa | Alessandro Zampogna | Fernanda Irrera

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