Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms

Y. L. Ng, X. Jiang, Y. Zhang, S. B. Shin, R. Ning

Abstract


Exoskeletons are wearable devices for enhancing human physical performance and for studying actions and movements. They are worn on the body for additional power and load-carrying capacity. Exoskeletons can be controlled using signals from the muscles. In recent years, gait analysis has attracted increasing attention from fields such as animation, athletic performance analysis, and robotics. Gait patterns are unique, and each individual has his or her own distinct gait pattern characteristics. Gait analysis can monitor activity in sensitive areas. This paper uses various machine learning algorithms to predict the activity of subjects using exoskeletons. Here, localization data from the UIC machine learning repository are used to recognize activities with gait positions. The study also compares five machine learning methods and examines their efficiency and accuracy in activity prediction for three different subjects. The results for the various machine learning methods along with efficiency and accuracy results are discussed.


Keywords


Exoskeletons; Gait analysis; Activity recognition; Machine learning algorithms

Full Text:

PDF

References


I. S. Dhindsa, R. Agarwal, H. S. Ryait, “A novel algorithm to predict knee angle from EMG signals for controlling a lower limb exoskeleton”, Information Technology and Nanotechnology, Samara, Russia, May, 2016

J. O. Brinker, T. Matsubara, T. Teramae, T. Noda, T. Ogasawarsa, T. Afour, J. Morimoto, “Walking Pattern Prediction with Partial Observation for Partial Walking Assistance by using an Exoskeleton System”, IEEE International Conference on Rehabilitation Robotics, Singapore, August 11-14, 2015

A. Zoss, H. Kazerooni, A. Chu, “On the Mechanical Design of the Berkeley Lower Extremity Exoskeleton (BLEEX)”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada, August 2-6, 2005

A. M. Dollar, H. Herr, “Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art”, IEEE Transactions on Robotics, Vol. 24, No. 1, pp. 144–158, 2008

M. J. Coren, “Robot exoskeletons are finally here, and they’re nothing like the suits from Iron Man”, available at: https://qz.com/971741/robot-exoskeletons-are-finally-here-and-theyre-nothing-like-the-suits-from-iron-man/, 2017 (accessed 27 June 2018)

V. Verma, A. Agarwal, “Exoskeleton Market Size, By Product (Stationary, Mobile), Type (Full Body, Upper Body, Lower Body), By Application (Industrial, Military, Healthcare) Industry Analysis Report, Regional Outlook (U.S., Canada, Germany, France, UK, Russia, China, Japan, South Korea, Brazil, Mexico, MEA), Application Development, Competitive Landscape & Forecast, 2017 – 2024”, available at: https://www.gminsights.com/industry-analysis/exoskeleton-market , 2017 (accessed 27 June 2018).

D. Torricelli, C. Cortes, N. Lete, A. Bertelsen, J. E. Gonzalez-Vargas, A. J. Del-Ama, I. Dimbwadyo, J. C. Moreno, J. Florez, J. L. Pons, “A subject-specific kinematic model to predict human motion in exoskeleton-assisted gait”, Frontiers in Neurorobotics, Vol. 12, 2018

E. Chong, F. C. Park, “Movement prediction for a lower limb exoskeleton using a conditional restricted Boltzmann machine”, Robotica, Vol. 35, No. 11 pp. 2177-2200, 2017

X. Zhang, M. Hashimoto, “SBC for motion assist using neural oscillator”, IEEE International Conference on Robotics and Automation, Kobe, Japan, May 12-17, 2009

R. Ronsse, N. Vitiello, T. Lenzi, J. van den Kieboom, M. C. Carrozza, A. J. Ijspeert, “Human-robot synchrony: Flexible assistance using adaptive oscillators”, IEEE Transactions on Biomedical Engineering, Vol. 58, No. 4, pp. 1001–1012, 2011

R. Ronsse, B. Koopman, N. Vitiello, T. Lenzi, D. Rossi, S. M. Maria, J. van den Kieboom, E. van Asseldonk, M. C. Carrozza, H. van der Kooij, A. J. Ijspeert, “Oscillator-based Walking Assistance: a Model-free Approach”, IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland, June 29-July 1, 2011

T. Matsubara, S. H. Hyon, J. Morimoto, “Real-time stylistic prediction for whole-body human motions”, Neural Networks, Vol. 25, pp. 191–199, 2012

T. Matsubara, A. Uchikata, J. Morimoto, “Full-body exoskeleton robot control for walking assistance by style-phase adaptive pattern generation”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal, October 7-12, 2012

T. Matsubara, D. Uto, T. Noda, T. Teramae, J. Morimoto, “Style-phase adaptation of human and humanoid biped walking patterns in real systems”, IEEE-RAS International Conference on Humanoid Robots (Humanoids), Madrid, Spain, November 18-20, 2014

H. A. Varol, F. Sup, M. Goldfarb, “Multiclass real-time intent recognition of a powered lower limb prosthesis”, IEEE Transactions on Biomedical Engineering, Vol. 57, No. 3, pp. 542–551, 2010

L. Wang, E. H. Van Asseldonk, H. Van Der Kooij, “Model Predictive Control-Based Gait Pattern Generation for Wearable Exoskeletons”, IEEE International Conference on Rehabilitation Robotics, Singapore, August 11-14, 2011

J. F. Veneman, R. Kruidhof, E. E. Hekman, R. Ekkelenkamp, E. H. van Asseldonk, H. van der Kooij, “Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 15, No. 3, pp. 379–386, 2007

H. B. Lim, T. P. Luu, K. H. Hoon, K. H. Low, “Natural gait parameters prediction for gait rehabilitation via artificial neural network IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 18-22, 2010

S. Jonic, T. Jankovic, V. Gajic, D. Popovic, “Three machine learning techniques for automatic determination of rules to control locomotion”, IEEE Transactions on Biomedical Engineering, Vol. 46, No. 3, pp. 300-310, 1999

S. Dutta, A. Chatterjee, S. Munshi, “An automated hierarchical gait pattern identification tool employing cross‐correlation‐based feature extraction and recurrent neural network based classification”, Expert Systems, Vol. 26, No. 2, pp. 202-217, 2009

https://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity




eISSN: 1792-8036     pISSN: 2241-4487