Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T17:38:58.609Z Has data issue: false hasContentIssue false
  • English
  • Français

Joint trajectory generator for powered orthosis based on gait modelling using PCA and FFT

Published online by Cambridge University Press:  06 November 2017

Nicholas B. Melo ,
Carlos E. T. Dórea ,
Pablo J. Alsina  and
Márcio V. Araújo
Nicholas B. Melo*
Affiliation:
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil. E-mails: [email protected], [email protected], [email protected]
Carlos E. T. Dórea
Affiliation:
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil. E-mails: [email protected], [email protected], [email protected]
Pablo J. Alsina
Affiliation:
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil. E-mails: [email protected], [email protected], [email protected]
Márcio V. Araújo
Affiliation:
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil. E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this work, we propose a method able to find user-oriented gait trajectories that can be used in powered lower limb orthosis applications. Most research related to active orthotic devices focuses on solving hardware issues. However, the problem of generating a set of joint trajectories that are user-oriented still persists. The proposed method uses principal component analysis to extract shared features from a gait dataset, taking into consideration gait-related variables such as joint angle information and the user's anthropometric features, used directly in an orthosis application. The trajectories of joint angles used by the model are represented by a given number of harmonics according to their respective Fourier series analyses. This representation allows better performance of the model, whose capability to generate gait information is validated through experiments using a real active orthotic device, analysing both joint motor energy consumption and user metabolic effort.

Keywords

RoboticsPowered orthosisAssistive roboticsTrajectory generatorHuman-driven assitive device
Type
Articles
Information
Robotica , Volume 36 , Issue 3 , March 2018 , pp. 395 - 407
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Dollar, A. M. and Herr, H., “Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art,” IEEE Trans Robot. 24 (1), 144–158 (2008).Google Scholar
2. Adam, B., Zoss, H. K. and Chu, A., “Biomechanical design of the berkeley lower extremity exoskeleton,” IEEE/ASME Trans. Mechatronics 11 (2), 128–138 (2006).Google Scholar
3. Talaty, M., Esquenazi, A. and Briceno, J. E., “Differentiating Ability in Users of the Rewalk Powered Exoskeleton: An Analysis of Walking Kinematics,” Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), IEEE, Seattle, USA (2013) pp. 1–5.Google Scholar
4. Araujo, M. V., Alsina, P. J., Roza, V. C. C. and Melo, N. B., “Powered orthosis ortholeg: Design and development,” IEEE Latin Am. Trans. 13 (1), 90–95 (2015).CrossRefGoogle Scholar
5. Krishnan, C., Ranganathan, R., Kantak, S. S., Dhaher, Y. Y. and Rymer, W. Z., “Active robotic training improves locomotor function in a stroke survivor,’. J. NeuroEngineering Rehabil. 9 (1), 57–70 (2012).CrossRefGoogle Scholar
6. Reinkensmeyer, D. J., Akoner, O. M., Ferris, D. P. and Gordon, K. E., “Slacking by the Human Motor System: Computational Models and Implications for Robotic Orthoses,” Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC, Minnesota, USA (2009) pp 2129–2132.Google Scholar
7. Arazpour, M., Hutchins, S. W. and Bani, M. A., “The efficacy of powered orthoses on walking in persons with paraplegia,” Prosthet. Orthot. Int. 39 (2), 90–99 (2015).Google Scholar
8. Kawamoto, H. and Sankai, Y., “Power Assist System Hal-3 for Gait Disorder Person,” Proceedings of the International Conference on Computers Helping People with Special Needs (ICCHP) (LectureNotes on Computer Science), Linz, Austria (2002) pp. 196–203.Google Scholar
9. Seddiki, L., Guelton, K., Zaytoon, J. and Akdag, H., “Trajectory generator design based on the user's intentions for a CMC lower-limbs rehabilitation device,” Robotica 34, 1026–1041 (2016).Google Scholar
10. Freivogel, S., Schmalohr, D. and Mehrholz, J., “Improved walking ability and reduced therapeutic stress with an electromechanical gait device,” J. Rehabil. Med. 41 (9), 734–743 (2009).Google Scholar
11. Yun, Y., Kim, K. H., Yul, S. S., Lee, J. and Kim, A. D. C, “Statistical method for prediction of gait kinematics with gaussian process regression,” J. Biomech. 47, 186–192 (2014).CrossRefGoogle ScholarPubMed
12. Hausdorff, J. M., Peng, C. K., Ladin, Z., Wei, J. Y. and Goldberger, A. L., “Is walking a random walk? Evidence for long-range correlations in stride interval of human gait,” J. Appl. Physiol. 78, 349–358 (1995).Google Scholar
13. Xiang, Y., Arora, S., Abdel, J. and Malek, K., “Optimization based prediction of asymmetric human gait,” J. Biomech. 44 (1), 683–693 (2011).Google Scholar
14. Endo, K. and Herr, H., “A model of muscle-tendon function in human walking at self-selected speed,” Model Muscle-Tendon Funct. Human Walking Self-Selected Speed 22 (2), 352–362 (2013).Google Scholar
15. Neptune, R. P., McGowan, C. A. and Kautz, S., “Forward dynamics simulations provide insight into muscle mechanical work during human locomotion,” Exercise Sport Sci. Rev. 37 (4), 203–210 (2009).Google Scholar
16. Castelan, M. and Arechavaleta, G., “Approximating The Reachable Space of Human Walking Paths: A Low Dimensional Linear Approach,” Proceedings of the 9th IEEE-RAS International Conference on Humanoid Robots, IEEE, Paris, France (2009) pp. 81–86.Google Scholar
17. Melo, N. B., Alsina, P. J., Dorea, C. E. T. and Araujo, M. V., “Gait Cycle Modeling for An Active Orthosis Using Principal Component Analysis,” Proceedings of the IEEE Latin American Robotics Symposium, IEEE, Arequipa, Peru (2013) pp. 118–123.Google Scholar
18. Trivino, A. A. G. and Cordon, O., “Human gait modeling using a genetic fuzzy finite state machine,” IEEE Trans. Fuzzy Syst. 20 (2), 205–223 (2012).Google Scholar
19. Ramirez, C. A., Castelan, M. and Arechavaleta, G., “Multilinear Decomposition of Human Walking Paths,” Proceedings of the IEEE-RAS International Conference on Humanoid Robots, IEEE, Nashville, USA (2010) pp. 492–497.Google Scholar
20. Glardon, P., Boulic, R. and Thalmann, D., “PCA-Based Walking Engine Using Motion Capture Data,” Proceedings of the Computer Graphics International, IEEE, Crete, Greece (2004) pp. 292–298.Google Scholar
21. Schwartz, M. H. and Rozumalski, A., “The gait deviation index: A new comprehensive index of gait pathology,” Gait Posture 28 (1), 351–357 (2008).CrossRefGoogle ScholarPubMed
22. Wang, L., Tan, T., Ning, H. and Hu, W., “Silhouette analysis based gait recognition for human identification,” IEEE Trans. Pattern Anal. Mach. Intell. 25 (12), 1505–1518 (2003).Google Scholar
23. Melo, N. B., Alsina, P. J., Dorea, C. E. T and Eugenio, K. J. S., “Influence of Different Gait Trajectories in an Lower Limb Active Orthosis Performance Based on User Metabolic Cost and Motors Usage,” Proceedings of the 26th 2015 International Symposium on Micro-NanoMechatronics and Human Science, IEEE, Nagoya, Japan (2015) pp. 1–6.Google Scholar
24. Jolliffe, I., Principal Component Analysis (Springer-Verlag, New York, USA, 2002) pp. 488.Google Scholar
25. Perry, J. and Burnfield, J. M. Gait Analysis: Normal and Pathological Function, 2nd ed. (Slack Inc, Thorofare, USA, 2010) p. 551.Google Scholar
26. Stuetzle, W., Cross-Validation, Encyclopedia of Statistics in Behavioral Science (John Wiley and Sons Inc, Seattle, USA, 2005).Google Scholar
27. Haykin, S. and Veen, B. V., Signals and Systems, 2nd ed. (Wiley, New York, USA, 2002) p. 802.Google Scholar
28. Chiu, M. C. and Wang, M. J., “The effect of gait speed and gender on perceived exertion, muscle activity, joint motion of lower extremity, ground reaction force and heart rate during normal walking,” Gait Posture 25 (3), 385–392 (2007).Google Scholar
29. Melo, N. B., Dorea, C. E. T., Alsina, P. J. and Eugenio, K. J. S., “Metabolic Effort Based on Heart Rate in User of the Ortholeg Active Orthosis,” Proceedings of the Brazilian Symposium on Intelligent Automation, SBA, Natal, Brazil (2015) pp. 1–5.Google Scholar
30. Elgendi, M., “Standard terminologies for photoplethysmogram signals,” Curr. Cardiology Rev. 8 (3), 215–219 (2012).CrossRefGoogle ScholarPubMed
31. “World Medical Association,” WMA Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects, [Online], Available: www.wma.net, March 31, 2016.Google Scholar
32. Arazpour, M., Chitsazan, A. and Hutchins, S. W., “Design and simulation of a new powered gait orthosis for paraplegic patients,” Prosthet. Orthot. Int. 36 (1), 125–130 (2012).Google Scholar
33. Fox, S. M., Naughton, J. P. and Haskell, W. L., “Physical activity and the prevention of coronary heart disease,” Ann. Clin. Res. 3 (1), 404–432 (1971).Google Scholar
34. Tanaka, H., Monahan, K. G. and Seals, D. S., “Age predicted maximal heart rate revisited,” Am. College Cardiology Found. 37 (1), 153–159 (2001).Google Scholar