Intelligent Systems for Rehabilitation Engineering

Roshani Raut, PhD is an associate professor in the Department of Information Technology at Pimpri Chinchwad College of Engineering, Pune University, India. She has presented and published more than 70 research communications in national/ international conferences and journals and has published 13 patents.Pranav D. Pathak, PhD from Visveswaraya National Institute of Technology, Nagpur, India. He is currently an assistant professor at MIT School of Bioengineering Sciences & Research, Pune. Sandeep Kautish, PhD in Computer Science on Intelligent Systems in Social Networks is Professor & Dean of Academics with LBEF Campus, Kathmandu Nepal. He has published more than 40 papers in international journals. Pradeep N., PhD is an associate professor in Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Karnataka, India. He has a number of edited books and journal research articles to his credit.

• Preface xiii1 Different Spheres of Rehabilitation Robotics: A Brief Survey Over the Past Three Decades 1Saumyadip Hazra, Abhimanyu Kumar, Yashonidhi Srivastava and Souvik Ganguli1.1 Introduction 11.2 An Overview of Robotics for Medical Applications 31.2.1 Neurological and Cognitive 31.2.2 Stroke Patients 31.2.3 Biomechanical or Mechatronic Robotic Systems 41.2.4 Human–Machine Interfacing 51.2.5 Smart Robotics 51.2.6 Control and Stability Analysis of Robotic Systems 71.2.7 Assistive Robotic Systems 91.2.8 Limb Injury 91.2.9 Motion Detection 91.3 Discussions and Future Scope of Work 101.4 Conclusion 12References 122 Neurorehabilitation Robots Review: Towards a Mechanized Process for Upper Limb 19Yogini Dilip Borole and Roshani Raut2.1 Introduction 192.2 Recovery and the Robotics 232.2.1 Automated Technological Tools Used in Rehabilitation 242.2.1.1 Exoskeletal-Type RTT 242.2.1.2 End-Effector-Type RTT 252.2.2 Benefits of the RTTs 252.3 New Directions to Explore and Open Problems: Aims of the Editorial 262.3.1 New Directions of Research and Development and First Aim of the Editorial 262.3.2 Open Problems and Second Aim of the Editorial 272.4 Overview 282.5 Renewal Process 292.5.1 Renovation Team 302.5.2 Renewal Methods and Results 302.6 Neurological Rehabilitation 312.6.1 Evaluation 312.6.2 Treatment Planning 332.6.3 Mediation 342.6.4 Assessment 342.7 State-of-the-Art Healthcare Equipment 352.7.1 Neuro Renewal of Upper Limb 352.7.1.1 Things and Method 352.7.2 Advanced Equipment for Neuro Revival of the Upper Limb 352.7.2.1 Methods of Testing 372.7.2.2 Renewal Methods and Results 382.8 Towards Autonomous Restoration Processes? 392.8.1 Default Renewal Cycle 402.8.1.1 Computerized Testing Programs 422.8.1.2 Choice Support System 432.8.1.3 Mechanical Rehabilitation Systems 452.9 Conclusion 46References 473 Competent and Affordable Rehabilitation Robots for Nervous System Disorders Powered with Dynamic CNN and HMM 57Sundaresan Sabapathy, Surendar Maruthu, Suresh Kumar Krishnadhas, Ananth Kumar Tamilarasan and Nishanth Raghavan3.1 Introduction 583.2 Related Works 593.2.1 Rehabilitation Robot for Lower Limbs 593.2.2 Rehabilitation Using Hip Bot 603.2.3 Rehabilitation Wrist Robot Using MRI Compatibility 613.2.4 Rehabilitation Robot for Gait Training 623.3 Solutions and Methods for the Rehabilitation Process 633.3.1 Gait Analysis 633.3.2 Methods Based on Deep Learning 643.3.3 Use of Convolutional Neural Networks 643.4 Proposed System 653.4.1 Detection of Motion and Rehabilitation Mechanism 663.4.2 Data Collection Using Wearable Sensors 683.4.3 Raspberry Pi 683.4.4 Pre-Processing of the Data 683.5 Analysis of the Data 693.5.1 Feature Extraction 693.5.2 Machine Learning Approach 703.5.3 Remote Rehabilitation Mode 713.6 Results and Discussion 723.7 Conclusion 90References 904 Smart Sensors for Activity Recognition 95Rehab A. Rayan, Imran Zafar, Aamna Rafique and Christos Tsagkaris4.1 Introduction 954.2 Wearable Biosensors for Activity Recognition 984.3 Smartphones for Activity Recognition 1004.3.1 Early Analysis Activity Recognition 1014.3.2 Similar Approaches Activity Recognition 1014.3.3 Multi-Sensor Approaches Activity Recognition 1024.3.4 Fitness Systems in Activity Recognition 1034.3.5 Human–Computer Interaction Processes in Activity Recognition 1044.3.6 Healthcare Monitoring in Activity Recognition 1044.4 Machine Learning Techniques 1054.4.1 Decision Trees Algorithms for Activity Reorganization 1064.4.2 Adaptive Boost Algorithms for Activity Reorganization 1064.4.3 Random Forest Algorithms for Activity Reorganization 1064.4.4 Support Vector Machine (SVM) Algorithms for Activity Reorganization 1064.5 Other Applications 1074.6 Limitations 1084.6.1 Policy Implications and Recommendations 1094.7 Discussion 1094.8 Conclusion 110References 1105 Use of Assistive Techniques for the Visually Impaired People 115Anuja Jadhav, Hirkani Padwad, M.B. Chandak and Roshani Raut5.1 Introduction 1155.2 Rehabilitation Procedure 1175.3 Development of Applications for Visually Impaired 1215.4 Academic Research and Development for Assisting Visually Impaired 1235.5 Conclusion 125References 1256 IoT-Assisted Smart Device for Blind People 129Roshani Raut, Anuja Jadhav, Swati Jaiswal and Pranav Pathak6.1 Introduction 1296.1.1 A Convolutional Neural Network 1306.1.2 CNN’s Operation 1316.1.3 Recurrent Neural Network 1346.1.4 Text-to-Speech Conversion 1346.1.5 Long Short-Term Memory Network 1346.2 Literature Survey 1386.3 Smart Stick for Blind People 1386.3.1 Hardware Requirements 1406.3.1.1 Ultrasonic Sensor 1406.3.1.2 IR Sensor 1416.3.1.3 Image Sensor 1416.3.1.4 Water Detector 1416.3.1.5 Global System for Mobile Communication 1426.3.1.6 Microcontroller Based on the Raspberry Pi 3 1426.4 System Development Requirements 1436.4.1 Captioning of Images 1436.4.2 YOLO (You Only Look Once) Model 1436.5 Features of the Proposed Smart Stick 1456.6 Code 1466.7 Results 1476.8 Conclusion 147References 1487 Accessibility in Disability: Revolutionizing Mobile Technology 151Nisarg Gandhewar and Senthilkumar Mohan7.1 Introduction 1527.2 Existing Accessibility Features for Mobile App and Devices 1537.2.1 Basic Accessibility Features and Services for Visually Impaired 1547.2.2 Basic Accessibility Features and Services for Deaf 1557.2.3 Basic Accessibility Features and Services for Cognitive Disabilities 1587.2.4 Basic Accessibility Features and Services for Physically Disabled 1597.3 Services Offered by Wireless Service Provider 1607.3.1 Digital Libraries for Visual 1617.3.2 GPS 1617.3.3 Relay Services 1627.3.4 Living With Independent 1627.3.5 Emergency Phone Services 1627.3.6 Customer Service 1627.4 Mobile Apps for a Person With Disability 1627.5 Technology Giants Providing Services 1667.5.1 Japan: NTT DoCoMo 1697.6 Challenges and Opportunities for Technology Giants to Provide Product & Service 1697.6.1 Higher Illiteracy Rate 1697.6.2 Reach out to Customers With Disabilities 1707.6.3 Higher Cost of Mobile Phones With Accessibility Features 1707.6.4 Increasing Percentage of Disability 1707.6.5 Unavailability of Assistive Technology in Regional Languages 1707.6.6 Lack of Knowledge Concerning Assistive Solutions 1717.7 Good Practices for Spreading Awareness 1717.8 Conclusion 172References 1728 Smart Solar Power–Assisted Wheelchairs For the Handicapped 175Abhinav Bhatnagar, Sidharth Pancholi and Vijay Janyani8.1 Introduction 1768.2 Power Source 1788.2.1 Solar-Powered Wheelchair 1798.2.2 Solar Energy Module 1808.3 Smart EMG-Based Wheelchair Control System 1828.3.1 Techniques of EMG Signal Collection 1848.3.2 Pre-Possessing and Segmentation of EMG Signal 1858.3.3 Feature Extraction and Pattern Classification 1868.3.3.1 Linear Discriminant Analysis (LDA) 1888.3.3.2 Support Vector Machine (SVM) 1888.3.3.3 Neural Network (NN) 1898.3.3.4 Random Forest (RF) 1898.4 Smart Navigation Assistance 1898.5 Internet of Things (IoT)–Enabled Monitoring 1908.6 Future Advancements in Smart Wheelchairs 191References 1929 Hand-Talk Assistance: An Application for Hearing and Speech Impaired People 197Pradnya Borkar, Vijaya Balpande, Ujjwala Aher and Roshani Raut9.1 Introduction 1989.1.1 Sign Language 1999.1.1.1 American Sign Language (ASL) 1999.1.1.2 Comparison of ASL With Verbal Language 1999.1.2 Recognition of Hand Gesture 2029.1.3 Different Techniques for Sign Language Detection 2029.1.3.1 Glove-Based Systems 2029.1.3.2 Vision-Based Systems 2029.2 Related Work 2039.3 History and Motivation 2049.4 Types of Sensors 2059.4.1 Flex Sensor 2059.4.1.1 Flex Sensor’s Specification 2059.4.1.2 Flex Sensor Types 2069.5 Working of Glove 2139.5.1 Hand Gloves 2139.5.2 Implementation Details at Server Side 2149.5.2.1 Training Mode 2149.5.2.2 Detection Mode 2159.5.2.3 Text to Speech 2159.6 Architecture 2169.7 Advantages and Applications 220References 22110 The Effective Practice of Assistive Technology to Boom Total Communication Among Children With Hearing Impairment in Inclusive Classroom Settings 223Fr. Baiju Thomas10.1 Introduction 22410.2 Students With Hearing Impairment 22510.3 The Classifications on Hearing Impairment 22610.3.1 Conductive Hearing Losses 22610.3.2 Sensorineural Hearing Losses 22710.3.3 Central Hearing Losses 22710.3.4 Mixed Hearing Losses 22710.4 Inclusion of Hearing-Impaired Students in Inclusive Classrooms 22910.4.1 Assistive Technology 22910.4.2 Assistive Technology for Hearing Impairments 23010.4.3 Hearing Technology 23110.4.4 Assistive Listening Devices 23110.4.5 Personal Amplification 23210.4.6 Communication Supports 23310.5 Total Communication System for Hearing Impairments 23310.6 Conclusion 236References 236Index 239