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ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal

Copyright © 2012. 21 pages.
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DOI: 10.4018/978-1-61350-429-1.ch020
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MLA

Naik, Ganesh, Dinesh Kant Kumar and Sridhar Arjunan. "ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal." Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies. IGI Global, 2012. 367-387. Web. 31 Oct. 2014. doi:10.4018/978-1-61350-429-1.ch020

APA

Naik, G., Kumar, D. K., & Arjunan, S. (2012). ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal. In V. Mago, & N. Bhatia (Eds.) Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies (pp. 367-387). Hershey, PA: Information Science Reference. doi:10.4018/978-1-61350-429-1.ch020

Chicago

Naik, Ganesh, Dinesh Kant Kumar and Sridhar Arjunan. "ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, ed. Vijay Kumar Mago and Nitin Bhatia, 367-387 (2012), accessed October 31, 2014. doi:10.4018/978-1-61350-429-1.ch020

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Abstract

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.
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Introduction

Hand actions and maintained gestures are a result of complex combination of contraction of multiple muscles in the forearm. There are numerous possible applications that are based on reliable identification of hand gestures including prosthesis control, human computer interface and games. There are three major modes of identification of the hand gestures;

  • 1.

    mechanical sensors e.g. - sensor gloves. Pavlovic et al (1997) noted that, ideally, naturalness of the interface requires that any and every gesture performed by the user should be interpretable. The use of glove requires an external device and it also needs the user to noticeably move their limbs in a way that may not be convenient, especially in case of amputees. For amputees, the control commands should be based on the intent of the movement rather than the mechanical movement.

  • 2.

    vision data with video analysis (Rehg & Kanade, 1993; Schlenzig, Hunter, & Jain, 1994). The state of the art in vision-based gesture recognition is far from providing a satisfactory solution to this problem. A major reason obviously is the complexity associated with the analysis and recognition of gestures. The video sensing is dependent on lighting conditions and unsuitable for very small gestures.

  • 3.

    muscle electrical activity (Cheron, Draye, Bourgeios, & Libert, 1996; Koike & Kawato, 1996). Surface Electromyography (sEMG) is the electrical recording of the muscle activity from the surface. It is closely related to the strength of muscle contraction and an obvious choice for control of the prosthesis and other similar applications.

Many attempts have been made to use sEMG signal as the command to control the prosthesis (Doerschuk, Gustafon, & Willsky, 1983; Wheeler & Jorgensen, 2003), but to obtain a reliable command signal, the muscle needs to have high level of contraction and with only one primary muscle being active. These techniques are not suitable for gestures where the muscle activity is small and there are multiple muscles active simultaneously such as during maintained hand gestures. This is largely attributable to the high level of cross-talk and low signal to noise ratio, both of which are more significant when the muscle activation level is low. To reliably identify the small movements and gesture of the hand, there is a need to decompose sEMG into muscle activity originating from the different muscles. Spectral and temporal filtering is not suitable for this because of overlapping spectrum and simultaneously active muscles. Blind source separation (BSS) techniques have recently been developed and these provide a solution for such a situation.

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Complete Chapter List

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Table of Contents
Preface
Vijay Kumar Mago, Nitin Bhatia
Chapter 1
Rigas Kouskouridas, Antonios Gasteratos
Recognizing objects in a scene is a fundamental task in image understanding. The recent advances in robotics and related technologies have placed... Sample PDF
From Object Recognition to Object Localization
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Chapter 2
Carlos Eduardo Thomaz, Vagner do Amaral, Gilson Antonio Giraldi, Edson Caoru Kitani, João Ricardo Sato, Duncan Gillies
This chapter describes a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity... Sample PDF
A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images
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Chapter 3
G.A. Papakostas, E.G. Karakasis, D.E. Koulouriotis
This chapter focuses on the usage of image orthogonal moments as discrimination features in pattern recognition applications and discusses their... Sample PDF
Orthogonal Image Moment Invariants: Highly Discriminative Features for Pattern Recognition Applications
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Chapter 4
Graziano Chesi, Yeung Sam Hung
Triangulation is a fundamental problem in computer vision that consists of estimating the 3D position of a point of the scene from the estimates of... Sample PDF
Certain and Uncertain Triangulation in Multiple Camera Vision Systems via LMIs
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Chapter 5
José Alexandre de França, Marcelo Ricardo Stemmer, Maria B. de Morais França, Rodrigo H. Cunha Palácios
Camera calibration is a process that allows to fully understand how the camera forms the image. It is necessary especially when 3D information of... Sample PDF
Camera Calibration with 1D Objects
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Chapter 6
Sang-Myoung Ye, Rae-Hong Park, Dong-Kyu Lee
Object segmentation in video sequence is a basic and important task in video applications such as surveillance systems and video coding.... Sample PDF
Object Segmentation Based on a Nonparametric Snake with Motion Prediction in Video
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Chapter 7
C.J. Prabhakar
The major contribution of the research work presented in this chapter is the development of effective face recognition algorithm using analysis of... Sample PDF
Analysis of Face Space for Recognition using Interval-Valued Subspace Technique
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Chapter 8
Yuexing Han, Bing Wang, Hideki Koike, Masanori Idesawa
One of the main goals of image understanding and computer vision applications is to recognize an object from various images. Object recognition has... Sample PDF
Object Recognition with a Limited Database Using Shape Space Theory
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Chapter 9
Abhishek Verma, Chengjun Liu
In this chapter, the authors propose and implement an improved iris recognition method based on image enhancement and heuristics. They make major... Sample PDF
Efficient Iris Identification with Improved Segmentation Techniques
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Chapter 10
P. S. Hiremath, Iranna Y. Humnabad
The study of medical image analysis encompasses the various techniques for acquisition of images of biological structures pertaining to human body... Sample PDF
Color Image Segmentation of Endoscopic and Microscopic Images for Abnormality Detection in Esophagus
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Chapter 11
T. Ravindra Babu, Chethan S.A. Danivas, S.V. Subrahmanya
Face Recognition is an active research area. In many practical scenarios, when faces are acquired without the cooperation or knowledge of the... Sample PDF
Adaptive Face Recognition of Partially Visible Faces
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Chapter 12
Sridhar Arjunan, Dinesh Kant Kumar, Hans Weghorn, Ganesh Naik
The need for developing reliable and flexible human computer interface is increased and applications of HCI have been in each and every field. Human... Sample PDF
Facial Muscle Activity Patterns for Recognition of Utterances in Native and Foreign Language: Testing for its Reliability and Flexibility
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Chapter 13
Deepak Sharma, Ekta Walia, H.P. Sinha
An accurate Content Based Image Retrieval (CBIR) system is essential for the correct retrieval of desired images from the underlying database.... Sample PDF
Feature Set Reduction in Rotation Invariant CBIR Using Dual-Tree Complex Wavelet Transform
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Chapter 14
P. Mukherji, P.P. Rege
Devnagari script is the most widely used script in India and its Optical Character Recognition (OCR) poses many challenges. Handwritten script has... Sample PDF
Devnagari Script Recognition: Techniques and Challenges
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Chapter 15
Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros
Reliable corner detection is an important task in pattern recognition applications. In this chapter an approach based on fuzzy-rules to detect... Sample PDF
Corner Detection Using Fuzzy Principles
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Chapter 16
Shuo Chen, Chengjun Liu
Eye detection is an important initial step in an automatic face recognition system. Though numerous eye detection methods have been proposed, many... Sample PDF
Eye Detection Using Color, Haar Features, and Efficient Support Vector Machine
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Chapter 17
Amit Konar, Aruna Chakraborty, Pavel Bhowmik, Sauvik Das, Anisha Halder
This chapter proposes new approaches to emotion recognition from facial expression and electroencephalogram signals. Subjects are excited with... Sample PDF
Emotion Recognition from Facial Expression and Electroencephalogram Signals
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Chapter 18
Artem A. Lenskiy, Jong-Soo Lee
In this chapter, the authors elaborate on the facial image segmentation and the detection of eyes and lips using two neural networks. The first... Sample PDF
Detecting Eyes and Lips Using Neural Networks and SURF Features
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Chapter 19
Sung Hee Park
This chapter presents a new method for binary classification that classifies input data into two regions separated by axis-aligned rectangular... Sample PDF
Classification with Axis-Aligned Rectangular Boundaries
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Chapter 20
Ganesh Naik, Dinesh Kant Kumar, Sridhar Arjunan
In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and... Sample PDF
ICA as Pattern Recognition Technique for Gesture Identification: A Study Using Bio-Signal
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Chapter 21
Pavel Holecek, Jana Talašová, Ivo Müller
This chapter describes a system of fuzzy methods designed to solve a broad range of problems in multiple-criteria evaluation, and also their... Sample PDF
Fuzzy Methods of Multiple-Criteria Evaluation and Their Software Implementation
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Chapter 22
Mamta Khosla, R K Sarin, Moin Uddin, Satvir Singh, Arun Khosla
In this chapter, the authors have realized Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) with the average of two Type-1 Fuzzy Logic Systems (T1... Sample PDF
Realizing Interval Type-2 Fuzzy Systems with Type-1 Fuzzy Systems
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Chapter 23
Ruchika Malhotra, Arvinder Kaur, Yogesh Singh
There are available metrics for predicting fault prone classes, which may help software organizations for planning and performing testing... Sample PDF
Comparative Analysis of Random Forests with Statistical and Machine Learning Methods in Predicting Fault-Prone Classes
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Chapter 24
Siddhartha Bhattacharyya
These networks generally operate in two different modes, viz., supervised and unsupervised modes. The supervised mode of operation requires a... Sample PDF
Neural Networks: Evolution, Topologies, Learning Algorithms and Applications
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Chapter 25
Mohammad Hossein Fazel Zarandi, Milad Avazbeigi
This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new... Sample PDF
A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling
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Chapter 26
Kandarpa Kumar Sarma, Abhijit Mitra
Artificial Neural Network (ANN) is a non-parametric statistical tool which can be used for a host of pattern classification and prediction problems.... Sample PDF
Estimation of MIMO Wireless Channels Using Artificial Neural Networks
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Chapter 27
E. Grace Mary Kanaga, M.L. Valarmathi, Preethi S.H. Darius
This chapter presents a novel 3D approach for patient scheduling (3D-PS) using multi-agents. Here the 3Ds refers to the Distributed, Dynamic and... Sample PDF
A Novel 3D Approach for Patient Schedule Using Multi-Agent Coordination
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Chapter 28
Jan Stoklasa
The decision making process of the Emergency Medical Rescue Services (EMRS) operations centre during disasters involves a significant amount of... Sample PDF
A Fuzzy Approach to Disaster Modeling: Decision Making Support and Disaster Management Tool for Emergency Medical Rescue Services
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Chapter 29
Elpiniki I. Papageorgiou
In this study, the fuzzy causal map inference mechanisms are analyzed for decision making tasks and a comparative analysis is performed to handle... Sample PDF
Fuzzy Cognitive Map Reasoning Mechanism for Handling Uncertainty and Missing Data: Application in Medical Diagnosis
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Chapter 30
Kostas Kolomvatsos, Stathes Hadjiefthymiades
Today, there is a large number of product providers in the Web. Electronic Marketplaces (EMs) enable entities to negotiate and trade products.... Sample PDF
On the Use of Fuzzy Logic in Electronic Marketplaces
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Chapter 31
Mohammad Hossein Fazel Zarandi, Milad Avazbeigi, Meysam Alizadeh
In today’s competitive markets, prediction of financial variables has become a critical issue. Especially in stock market analysis where a wrong... Sample PDF
A Neuro-Fuzzy Expert System Trained by Particle Swarm Optimization for Stock Price Prediction
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Chapter 32
Koushik Bakshi, Sourav Chandra, Amit Konar, D.N. Tibarewala
This chapter provides a prototype design of a hand tremor compensator/controller to reduce the effect of the tremor to an external device/... Sample PDF
Hand Tremor Prediction and Classification Using Electromyogram Signals to Control Neuro-Motor Instability
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