Publication Type : Conference Paper
Thematic Areas : Learning-Technologies, Medical Sciences, Biotech
Publisher : Proceedings of the Sixth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2017, Manipal University, Karnataka, India
Source : Proceedings of the Sixth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2017, Manipal University, Karnataka, India, Sept 13-16, 2017.
Url : https://scholar.google.com/scholar?oi=bibs&cluster=4348042297679788700&btnI=1&hl=en
Keywords : Acceleration, accelerometer data, Accelerometers, antecubitis, axial positions, biometric technique, carpus, classification, Classification algorithms, closed room environment, coxal, Data mining, Diseases, exoskeleton technologies, Feature extraction, feature ranking, Gait analysis, gait feature classification, Gait patterns, image classification, learning (artificial intelligence), Learning algorithms, Legged locomotion, Machine learning, Machine learning algorithms, machine learning optimal feature subselection, multiple accelerometers, stance, structured gait patterns, swing, swing phase, tarsus
Campus : Amritapuri
School : School of Biotechnology
Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology
Department : Computational Neuroscience Laboratory, biotechnology
Year : 2017
Abstract : Structured gait patterns are currently used as a biometric technique to recognize individuals and in building appropriate exoskeleton technologies. In this study, the features involved in gait were extracted and analyzed. Multiple accelerometers were used to collect the data which was then used to identify gait at various axial positions form healthy volunteers with total of 60 trails. Using machine learning optimal feature sub-selection we analyze data to implicate the optimal methods for analysis of swing phase and stance phase in a closed room environment. Study reports that the accelerometer data could classify based on the accuracy and the efficiency of the learning algorithms. Through feature ranking, results suggest gait can be attributed to a combination of Brachium of arm, Antecubitis, Carpus, Coxal, Femur and Tarsus (Shoulder, Elbow, Wrist, Hip, Knee, and Ankle). This gait study may help analyzing conditions during control and movement-related disease.
Cite this Research Publication : Chaitanya Nutakki, Jyothisree Narayanan, Aswathy Anitha Anchuthengil, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Classifying gait features for stance and swing using machine learning”, in Proceedings of the Sixth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2017), Manipal University, Karnataka, India, Sept 13-16, 2017.