Abstract
The main objective of this paper illustrates an elementary concept about the designing, development and implementation of a bio-informatics diagnostic tool which understands and analyzes the human gait oscillation in order to provide an insight on human bi-pedal locomotion and its stability. A multi sensor device for detection of gait oscillations during human locomotion has been developed effectively. It has been named “IGOD”, an acronym of the “Intelligent Gait Oscillation Detector”. It ensures capturing of different person’s walking pattern in a very elegant way. This device would be used for creating a database of gait oscillations which could be extensively applied in several implications. The preliminary acquired data for eight major joints of a human body have been presented significantly. The electronic circuit has been attached to IGOD device in order to customize the proper calibration of every joint angle eventually.
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References
Taga, G., Yamaguchi, Y., Shimizu, H.: Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics 65(3), 147–159 (1991)
Su, H., Huang, F.-G.: Human gait recognition based on motion analysis. Proceedings of International Conference on Machine Learning and Cybernetics 7(18-21), 4464–4468 (2005)
Cunado, D., Nixon, M.S., Carter, J.N.: Automatic extraction and description of human gait models for recognition purposes. Computer Vision and Image Understanding 90(1), 1–41 (2003)
Riley, M., Ude, A., Wade, K., Atkeson, C.G.: Enabling real-time full-body imitation: a natural way of transferring human movement to humanoids. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 2(14-19), pp. 2368–2374 (2003)
Lee, J., Ha, I.: Real-Time Motion Capture for a Human Body using Accelerometer. In: Robotica, vol. 19, pp. 601–610. Cambridge University Press, Cambridge (2001)
Lee, J., Ha, I.: Sensor Fusion and Calibration for Motion Captures using Accelerometers. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 3, pp. 1954–1959 (1999)
Barbieri, R., Farella, E., Benini, L., Ricco, B., Acquaviva, A.: A low-power motion capture system with integrated accelerometers (gesture recognition applications). In: Consumer Communications and Networking Conference, vol. 1(5-8), pp. 418–423 (2004)
Hafner, V.V., Bachmann, F.: Human-Humanoid walking gait recognition. In: Proceedings of 8th IEEE-RAS International Conference on Humanoid Robots, pp. 598–602 (2008)
Au, S.K., Dilworth, P., Herr, H.: An ankle-foot emulation system for the study of human walking biomechanics. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2939–2945 (2006)
Nandi, G.C., Ijspeert, A., Nandi, A.: Biologically inspired CPG based above knee active prosthesis. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2368–2373 (2008)
Lugo-Villeda, L.I., Frisoli, A., Sandoval, G.O.O., Bergamasco, M., Parra-Vega, V.: A mechatronic analysis and synthesis of human walking gait. In: Proceedings of IEEE International Conference on Mechatronics, pp. 1–6 (2009)
Phidget Interface kit, http://www.phidgets.com/products.php?category=0&product_id=1018
Phidget Rotation Sensor, http://www.phidgets.com/products.php?category=1&product_id=1109
Lissajous_curve, http://en.wikipedia.org/wiki/Lissajous_curve
Nandi, G.C., Ijspeert, A., Chakraborty, P., Nandi, A.: Development of Adaptive Modular Active Leg (AMAL) using bipedal robotics technology. Robotics and Autonomous Systems 57(6-7), 603–616 (2009)
Yi, Z., Shayan, A., Wanping, Z., Tong, L., Chen, T.-P., Jung, J.-R., Duann, M.S., Chung-Kuan, C.: Analyzing High-Density ECG Signals Using ICA. IEEE Transactions on Biomedical Engineering 55(11), 2528–2537 (2008)
Yang, Q., Siemionow, V., Yao, W., Sahgal, V., Yue, G.H.: Single-Trial EEG-EMG Coherence Analysis Reveals Muscle Fatigue-Related Progressive Alterations in Corticomuscular Coupling. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18(2), 97–106 (2010)
Marzani, F., Calais, E., Legrand, L.: A 3-D marker-free system for the analysis of movement disabilities - an application to the legs. IEEE Transactions on Information Technology in Biomedicine 5(1), 18–26 (2001)
Green, R.D., Ling, G.: Quantifying and recognizing human movement patterns from monocular video Images-part I: a new framework for modeling human motion. IEEE Transactions on Circuits and Systems for Video Technology 14(2), 179–190 (2004)
Dejnabadi, H., Jolles, B.M., Aminian, K.: A New Approach for Quantitative Analysis of Inter-Joint Coordination During Gait. IEEE Transactions on Biomedical Engineering 55(2), 755–764 (2008)
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Mondal, S., Nandy, A., Chakrabarti, A., Chakraborty, P., Nandi, G.C. (2010). A Framework for Synthesis of Human Gait Oscillation Using Intelligent Gait Oscillation Detector (IGOD). In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_32
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DOI: https://doi.org/10.1007/978-3-642-14834-7_32
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