An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results.

[1]  Rafiqul Zaman Khan,et al.  Survey on Various Gesture Recognition Technologies and Techniques , 2012 .

[2]  A O Posatskiy,et al.  Design and evaluation of a novel microphone-based mechanomyography sensor with cylindrical and conical acoustic chambers. , 2012, Medical engineering & physics.

[3]  Ke Liao,et al.  Decoding Individual Finger Movements from One Hand Using Human EEG Signals , 2014, PloS one.

[4]  Chih-Chen Chang,et al.  Structural Damage Assessment Based on Wavelet Packet Transform , 2002 .

[5]  Tom Chau,et al.  A self-contained, mechanomyography-driven externally powered prosthesis. , 2005, Archives of physical medicine and rehabilitation.

[6]  Laura Seidl,et al.  Determining minimal stimulus intensity for mechanomyographic analysis. , 2015, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[7]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[8]  Kenneth C. Mylrea,et al.  Investigation of Sounds Produced by Healthy and Diseased Human Muscular Contraction , 1986, IEEE Transactions on Biomedical Engineering.

[9]  D T Barry,et al.  Acoustic myography as a control signal for an externally powered prosthesis. , 1986, Archives of physical medicine and rehabilitation.

[10]  Desire L. Massart,et al.  Noise suppression and signal compression using the wavelet packet transform , 1997 .

[11]  Kapil D. Katyal,et al.  Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject , 2016, Journal of neural engineering.

[12]  Rupert Lanzenberger,et al.  Finger Somatotopy in Human Motor Cortex , 2001, NeuroImage.

[13]  Michael-Paul Schallmo,et al.  Selective BOLD responses to individual finger movement measured with fMRI at 3T , 2012, Human brain mapping.

[14]  K. Hong,et al.  Bundled-Optode Method in Functional Near-Infrared Spectroscopy , 2016, PloS one.

[15]  Hong-Bo Xie,et al.  Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control , 2009, Physiological measurement.

[16]  Thomas J. Watson,et al.  An empirical study of the naive Bayes classifier , 2001 .

[17]  Keum-Shik Hong,et al.  Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface , 2014, Front. Hum. Neurosci..

[18]  Tom Chau,et al.  Uncovering patterns of forearm muscle activity using multi-channel mechanomyography. , 2010, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[19]  Tao Han,et al.  ART–KOHONEN neural network for fault diagnosis of rotating machinery , 2004 .

[20]  Deba Pratim Saha.  Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals , 2013 .

[21]  Hellmuth Obrig,et al.  Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography , 2012, NeuroImage.

[22]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[23]  Yu Hongliu,et al.  MMG signal and its applications in prosthesis control , 2010 .

[24]  Joseph L. Hellerstein,et al.  Recognizing End-User Transactions in Performance Management , 2000, AAAI/IAAI.

[25]  Dario Farina,et al.  Upper trapezius muscle mechanomyographic and electromyographic activity in humans during low force fatiguing and non-fatiguing contractions , 2002, European Journal of Applied Physiology.

[26]  Alexandre Balbinot,et al.  Use of inertial sensors as devices for upper limb motor monitoring exercises for motor rehabilitation , 2015 .

[27]  Tom Chau,et al.  MMG-based multisensor data fusion for prosthesis control , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[28]  Natasha Alves-Kotzev.  Mechanomyography as an Access Pathway for Binary and Multifunction Control , 2011 .

[29]  A. Grossman,et al.  Functional mapping of multiple mechanomyographic signals to hand kinematics , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[30]  Qing He,et al.  Motion intent recognition of individual fingers based on mechanomyogram , 2017, Pattern Recognit. Lett..

[31]  M. Brenner.  Non-stationary dynamics data analysis with wavelet-SVD filtering , 2003 .

[32]  Nur Azah Hamzaid,et al.  Mechanomyography and muscle function assessment: a review of current state and prospects. , 2014, Clinical biomechanics.

[33]  Fabrizio Angiulli,et al.  Fast condensed nearest neighbor rule , 2005, ICML.

[34]  U. Rajendra Acharya,et al.  Finger Motion Classification by Forearm Skin Surface Vibration Signals , 2010, The open medical informatics journal.

[35]  Srinivasan Ramakrishnan,et al.  SVD-Based Modeling for Image Texture Classification Using Wavelet Transformation , 2007, IEEE Transactions on Image Processing.

[36]  C. Orizio,et al.  Spectral analysis of muscular sound during isometric contraction of biceps brachii. , 1990, Journal of applied physiology.

[37]  M. Watakabe,et al.  Mechanical behaviour of condenser microphone in mechanomyography , 2001, Medical and Biological Engineering and Computing.

[38]  Sanjib Kumar Panda,et al.  A Wavelet Feature Based Mechanomyography Classification System for a Wearable Rehabilitation System for the Elderly , 2013, ICOST.

[39]  M. A. Islam,et al.  Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review , 2013, IEEE Sensors Journal.

[40]  Keum Shik Hong,et al.  Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review , 2017, Front. Neurorobot..

[41]  M. R. Bhutta,et al.  Classification of somatosensory cortex activities using fNIRS , 2017, Behavioural Brain Research.

[42]  Tom Chau,et al.  Automatic detection of muscle activity from mechanomyogram signals: a comparison of amplitude and wavelet-based methods , 2010, Physiological measurement.

[43]  Francisco Sepulveda,et al.  A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue , 2011, Sensors.

[44]  J. Schenck.  The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. , 1996, Medical physics.

[45]  Markus Nowak,et al.  Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).

[46]  Zhang Xuegong,et al.  INTRODUCTION TO STATISTICAL LEARNING THEORY AND SUPPORT VECTOR MACHINES , 2000 .

[47]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[48]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.