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A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing
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DOI：10.1109/ACCESS.2019.2948090.png)
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Abstract
En 中文
This paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18-$\mu \text{m}$ complementary metal-oxide-semiconductor (CMOS) process. Based on this ROIC, a wearable device module and its wireless system prototype are implemented to recognize five kinds of real-time hand-gesture motions, where the power consumption is further reduced by adopting low-power components. Real-time measurements of sEMG signals and APWP data through this wearable interface are wirelessly transferred to a laptop or a sensor hub, and then they are further processed to implement the pseudo-wavelet transform under the MATLAB environment. The resulting APWP-augmented pattern-recognition algorithm was experimentally verified to improve the accuracy by 7 with a real-time frequency analysis.
Keywords:
Pattern recognition
Real-time systems
Wireless communication
Power demand
Wireless sensor networks
Wavelet transforms
Frequency-domain analysis
Surface electromyogram
pattern recognition
readout integrated circuit
analog wavelet preprocessor
wireless sensor interface
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