Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms
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 algorithmsDownloads
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 DOI: https://doi.org/10.18287/1613-0073-2016-1638-536-541
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 DOI: https://doi.org/10.1109/ICORR.2015.7281189
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 DOI: https://doi.org/10.1109/IROS.2005.1545453
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 DOI: https://doi.org/10.1109/TRO.2008.915453
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 DOI: https://doi.org/10.3389/fnbot.2018.00018
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 DOI: https://doi.org/10.1017/S0263574716000795
X. Zhang, M. Hashimoto, “SBC for motion assist using neural oscillator”, IEEE International Conference on Robotics and Automation, Kobe, Japan, May 12-17, 2009 DOI: https://doi.org/10.1109/ROBOT.2009.5152411
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 DOI: https://doi.org/10.1109/TBME.2010.2089629
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 DOI: https://doi.org/10.1109/ICORR.2011.5975352
T. Matsubara, S. H. Hyon, J. Morimoto, “Real-time stylistic prediction for whole-body human motions”, Neural Networks, Vol. 25, pp. 191–199, 2012 DOI: https://doi.org/10.1016/j.neunet.2011.08.008
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 DOI: https://doi.org/10.1109/IROS.2012.6385528
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 DOI: https://doi.org/10.1109/HUMANOIDS.2014.7041348
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 DOI: https://doi.org/10.1109/TBME.2009.2034734
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 DOI: https://doi.org/10.1109/TNSRE.2007.903919
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 DOI: https://doi.org/10.1109/IROS.2010.5650311
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 DOI: https://doi.org/10.1109/10.748983
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 DOI: https://doi.org/10.1111/j.1468-0394.2009.00479.x
https://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity
Downloads
How to Cite
License
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
