Modern Methods for Affordable Clinical Gait Analysis

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.

• 1. Introduction2. Statistics and computational intelligence in clinical gait analysis3. Low-cost sensors for gait analysis4. Validation study of low-cost sensors5. Gait segmentation and event detection techniques6. Methodologies for vision-based automatic pathological gait detection7. Pathological gait pattern analysis using inertial sensor8. A low-cost electromyography (EMG) sensor-based gait activity analysis9. Low-cost systems–based therapeutic intervention10. Prevention, rehabilitation, monitoring, and recovery prediction for musculoskeletal injuries11. Design and development of pathological gait assessment tools12. Conclusion