A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing

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.

[1]  Loredana Zollo,et al.  NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation , 2017, Journal of NeuroEngineering and Rehabilitation.

[2]  Othman Omran Khalifa,et al.  The Use of Artificial Neural Network in the Classification of EMG Signals , 2012, 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing.

[3]  Abdulhamit Subasi,et al.  Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders , 2014, Journal of Medical Systems.

[4]  Gabor C. Temes,et al.  Circuit techniques for reducing the effects of op-amp imperfections: autozeroing, correlated double sampling, and chopper stabilization , 1996, Proc. IEEE.

[5]  A. Tekkeşin.  Artificial Intelligence in Healthcare: Past, Present and Future. , 2019, Anatolian journal of cardiology.

[6]  Luca Benini,et al.  A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[7]  Pantelis Georgiou,et al.  A Muscle Fibre Conduction Velocity Tracking ASIC for Local Fatigue Monitoring , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[8]  Youn Tae Kim,et al.  A study of an EMG-based authentication algorithm using an artificial neural network , 2017, 2017 IEEE SENSORS.

[9]  Marco Paleari,et al.  Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography , 2014, PloS one.

[10]  Hoffmann Klaus-Peter,et al.  A SIC design of an implantable system for improved control of hand prosthesis , 2016 .

[11]  Juan Carlos Gonzalez-Ibarra,et al.  EMG Pattern Recognition System Based on Neural Networks , 2012, 2012 11th Mexican International Conference on Artificial Intelligence.

[12]  Refet Firat Yazicioglu,et al.  A $160~\mu {\rm W}$ 8-Channel Active Electrode System for EEG Monitoring , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[13]  J.H. Huijsing,et al.  A Chopper Current-Feedback Instrumentation Amplifier With a 1 mHz $1/f$ Noise Corner and an AC-Coupled Ripple Reduction Loop , 2009, IEEE Journal of Solid-State Circuits.

[14]  Dae Jung Kim,et al.  A Wireless ExG Interface for Patch-Type ECG Holter and EMG-Controlled Robot Hand , 2017, Sensors.

[15]  Lachit Dutta,et al.  F-SVD based algorithm for variability and stability measurement of bio-signals, feature extraction and fusion for pattern recognition , 2019, Biomed. Signal Process. Control..

[16]  Alexander J. Casson,et al.  An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes , 2015, Sensors.

[17]  Othman O. Khalifa,et al.  Hand motion detection from EMG signals by using ANN based classifier for human computer interaction , 2011, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization.

[18]  Lucia Rita Quitadamo,et al.  Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees , 2014, Biomed. Signal Process. Control..

[19]  Adrian D. C. Chan,et al.  Biosignal quality analysis of surface EMG using a correlation coefficient test for normality , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[20]  Guang-Bin Huang,et al.  Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions , 1998, IEEE Trans. Neural Networks.

[21]  Hoi-Jun Yoo,et al.  Bio-Medical CMOS ICs , 2011, Integrated Circuits and Systems.

[22]  Sebastian Amsuess,et al.  Ultra-Low-Power Digital Filtering for Insulated EMG Sensing , 2019, Sensors.

[23]  H. Landau.  Sampling, data transmission, and the Nyquist rate , 1967 .

[24]  Timothy H. Lucas,et al.  The Virtual Trackpad: An Electromyography-Based, Wireless, Real-Time, Low-Power, Embedded Hand-Gesture-Recognition System Using an Event-Driven Artificial Neural Network , 2017, IEEE Transactions on Circuits and Systems II: Express Briefs.

[25]  Guoxing Wang,et al.  A 10-bit 1kS/s-30kS/s successive approximation register analog-to-digital converter for biological signal acquisition , 2013, 2013 6th International Conference on Biomedical Engineering and Informatics.

[26]  Hariprasad Chandrakumar,et al.  A High Dynamic-Range Neural Recording Chopper Amplifier for Simultaneous Neural Recording and Stimulation , 2017, IEEE Journal of Solid-State Circuits.

[27]  Winnie Jensen,et al.  Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions , 2019, IEEE Journal of Biomedical and Health Informatics.

[28]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[29]  S. Gupta,et al.  Microwave Analog Real-Time Spectrum Analyzer (RTSA) Based on the Spectral–Spatial Decomposition Property of Leaky-Wave Structures , 2009, IEEE Transactions on Microwave Theory and Techniques.

[30]  Xu Zhang,et al.  Wavelet transform theory and its application in EMG signal processing , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[31]  Jeongjin Roh,et al.  Subband filtering for time and frequency analysis of mixed-signal circuit testing , 2004, IEEE Transactions on Instrumentation and Measurement.

[32]  Kyeonghwan Park,et al.  A 4b/cycle flash-assisted SAR ADC with comparator speed-boosting technique , 2018 .

[33]  Oluwarotimi Williams Samuel,et al.  A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control , 2018, IEEE Access.

[34]  Seungwook Lee,et al.  An Energy-Efficient Multimode Multichannel Gas-Sensor System With Learning-Based Optimization and Self-Calibration Schemes , 2020, IEEE Transactions on Industrial Electronics.