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Abstract
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-μm 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 handgesture 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.
Highlights
Recent wireless sensor systems for Internet of Things (IoT) applications aim to improve system efficiency by adopting the artificial intelligence (AI) to extract various events of interest from real-time retrieved data [1]
After an analog surface electromyography (sEMG) signal is sensed and amplified, it passes through the analog pseudo-wavelet preprocessor (APWP) where it is filtered for the wavelet transform or bypassed to the successive approximation register analogto-digital converter (SAR ADC) for digitization
SEMG DETECTION EXPERIMENTAL ENVIRONMENT Fig. 7 shows a flexible wearable module prototype based on the readout integrated circuit (ROIC) and five finger movements for pattern recognition
Summary
Recent wireless sensor systems for Internet of Things (IoT) applications aim to improve system efficiency by adopting the artificial intelligence (AI) to extract various events of interest from real-time retrieved data [1]. Chae et al.: Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing in the software side. This work presents a real-time wearable sEMG pattern-recognition interface to keep the accuracy high and optimize the processing in the hardware side at the same time.
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