Modern Methods for Affordable Clinical Gait Analysis: Theories and Applications in Healthcare Systems is a handbook of techniques, tools and procedures for the study and improvement of human gait. It gives a concise description of clinical gait analysis, especially gait abnormality detection problems and therapeutic interventions using inexpensive devices. A brief demonstration on validation testing of these devices for its clinical applicability is also presented. Content coverage also includes step-by-step processing of the data acquired from these devices. Future perspectives of low-cost clinical gait assessment systems are explored.
This book bridges the gap between engineering and biomedical fields as it diagnoses and monitors neuro-musculoskeletal abnormalities using the latest technologies. The authors discuss how early detection technology allows us to take precautionary measures, in order to delay the degeneration process, through development of a clinical gait analysis tool. One unique feature of this book is that it pays significant attention to the challenges of conducting gait analysis in developing countries with limited resources. This reference will guide you through setting up a low-cost gait analysis lab. It explores the relationship between vision-based pathological gait detection, the design of tools for gait diagnosis and therapeutic interventions.
- Provides a concise tutorial on affordable clinical gait analysis
- Analyses clinical validation of low-cost sensors for gait assessment
- Documents recent and state-of-the-art low-cost gait abnormality detection systems and therapeutic intervention procedures
| About the authors |
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ix | |
| Preface |
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xi | |
| Acknowledgment |
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xiii | |
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1 | (16) |
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1 | (1) |
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1 | (2) |
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3 | (1) |
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1.4 Model-based versus model-free gait assessment |
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4 | (5) |
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1.5 Applications of gait analysis |
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9 | (1) |
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1.6 Clinical aspects of human gait |
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10 | (2) |
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1.7 Sensors for gait data acquisition |
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12 | (1) |
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13 | (4) |
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13 | (4) |
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2 Statistics and computational intelligence in clinical gait analysis |
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17 | (8) |
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17 | (1) |
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2.2 Statistics in clinical gait data |
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17 | (3) |
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2.3 Computational intelligence in clinical gait data |
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20 | (2) |
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2.4 Statistics versus computational intelligence |
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22 | (1) |
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23 | (2) |
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23 | (2) |
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3 Low-cost sensors for gait analysis |
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25 | (20) |
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25 | (1) |
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3.2 Motion capture sensors for gait |
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25 | (2) |
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27 | (3) |
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30 | (10) |
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40 | (5) |
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40 | (5) |
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4 Validation study of low-cost sensors |
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45 | (12) |
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45 | (1) |
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4.2 Kinect validation for clinical usages |
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45 | (5) |
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4.3 Inertial sensor validation on estimating joint angles |
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50 | (3) |
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53 | (4) |
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54 | (3) |
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5 Gait segmentation and event detection techniques |
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57 | (14) |
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57 | (1) |
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5.2 Why gait cycle segmentation? |
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57 | (1) |
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5.3 Vision sensor-based gait cycle segmentation |
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58 | (3) |
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5.4 Kinect in gait cycle segmentation |
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61 | (1) |
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5.5 Inertial sensor-based gait segmentation |
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62 | (3) |
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5.6 Electromyography sensor-based gait segmentation |
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65 | (2) |
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67 | (4) |
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67 | (4) |
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6 Methodologies for vision-based automatic pathological gait detection |
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71 | (10) |
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71 | (1) |
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6.2 Gait detection techniques |
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72 | (3) |
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6.3 Automatic diagnostic systems using Kinect |
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75 | (1) |
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6.4 Gait diagnosis in multi-Kinect architecture |
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76 | (2) |
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78 | (3) |
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78 | (3) |
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7 Pathological gait pattern analysis using inertial sensor |
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81 | (20) |
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81 | (1) |
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82 | (2) |
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7.3 Gait signal segmentation |
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84 | (1) |
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7.4 Gait features using inertial sensor signals |
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84 | (3) |
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7.5 Automated feature extraction using deep learning techniques |
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87 | (1) |
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7.6 Gait pattern modeling using machine learning techniques |
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88 | (1) |
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89 | (7) |
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96 | (5) |
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97 | (4) |
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8 A low-cost electromyography (EMG) sensor-based gait activity analysis |
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101 | (28) |
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101 | (1) |
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8.2 Description of lower leg muscles |
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101 | (1) |
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8.3 Specification of MyoWare electromyography sensor |
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102 | (3) |
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8.4 Hardware requirement for electromyography experimental setup |
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105 | (5) |
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8.5 Preprocessing of electromyography signals |
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110 | (4) |
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8.6 Electromyography sensor-based feature analysis |
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114 | (4) |
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8.7 Gait analysis using surface electromyography sensors |
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118 | (6) |
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124 | (5) |
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125 | (4) |
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9 Low-cost systems---based therapeutic intervention |
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129 | (8) |
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129 | (1) |
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9.2 Kinect in therapeutic intervention |
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130 | (1) |
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9.3 Wearable sensors in therapeutic intervention |
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131 | (2) |
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133 | (4) |
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133 | (4) |
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10 Prevention, rehabilitation, monitoring, and recovery prediction for musculoskeletal injuries |
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137 | (10) |
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137 | (1) |
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10.2 Musculoskeletal injuries: causes and treatments |
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138 | (2) |
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10.3 Prevention of musculoskeletal injury through gait monitoring |
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140 | (2) |
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10.4 Rehabilitation monitoring for recovery prediction |
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142 | (2) |
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144 | (3) |
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144 | (3) |
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11 Design and development of pathological gait assessment tools |
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147 | (24) |
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147 | (1) |
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11.2 Tools for pathological gait assessment |
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148 | (1) |
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11.3 Development of a gait event annotation tool |
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149 | (9) |
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11.4 Development of a gait diagnosis tool |
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158 | (10) |
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168 | (3) |
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168 | (3) |
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171 | (4) |
| Index |
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Dr. Anup Nandy is working as an Assistant Professor (Grade I) in Department of Computer Science and Engineering at National Institute of Technology (NIT), Rourkela. He earned his PhD from Indian Institute of Information Technology, Allahabad, in the year of 2016. His research interest includes Artificial Intelligence, Machine Learning, Human Gait Analysis, Computing Human Cognition, and Robotics. He received an Early Career Research Award from SERB, Government of India in 2017 for conducting research on Human Cognitive State Estimation through Multimodal Gait Analysis. He received research funding for Indo-Japanese Bilateral research project, funded by DST, Government of India and JSPS, Japan, with joint collaboration of Tokyo University of Agriculture and Technology (TUAT). He received a prestigious NVIDIA GPU Grant Award in 2018 for his research on Gait Abnormality Detection using Deep Learning Techniques. He was selected as Indian Young Scientist in the thematic area of Artificial Intelligence to participate in fifth BRICS Conclave 2020 held at Chelyabinsk, Russia, from Sept 21e25, 2020. Recently, he received research grant from DST, Government of India and Ministry of Science and ICT of the Republic of Korea in February 2021 with joint collaboration of Korea Advanced Institute of Science and Technology. He has published a good number of research papers in reputed conferences and journals. Saikat Chakraborty obtained his MTech from Jadavpur University. Currently he is a PhD research scholar in the Computer Science and Engineering Department at NIT, Rourkela. Beside human gait analysis, he has research experience of two years in machine learning in the field of video summarization and sentiment analysis. His current research interests include computational neuroscience and computational biomechanics. He also worked as a visiting researcher in GV lab, TUAT, Japan. Jayeeta Chakraborty is a PhD scholar in the department of Computer Science and Engineering in NIT, Rourkela. Her current research interests include Machine Learning, Human Gait Analysis, Signal and Image Processing. She has previous research experience in the domain of Data Mining, Recommendation Systems, and Semantic Web. Gentiane Venture is a French Roboticist working in academia in Tokyo. She is a distinguished professor with TUAT and a cross appointed fellow with AIST. She obtained her MSc and PhD from Ecole Centrale/University of Nantes in 2000 and 2003, respectively. She worked at CEA in 2004 and for six years at the University of Tokyo. In 2009, she started with TUAT where she has established an international research group working on human science and robotics. With her group she conducts theoretical and applied research on motion dynamics, robot control, and nonverbal communication to study the meaning of living with robots. Her work is highly interdisciplinary, collaborating with therapists, psychologists, neuroscientists, sociologists, philosophers, ergonomists, artists, and designers.
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