Hostname: page-component-7b7567c8c-2n9c9 Total loading time: 0 Render date: 2026-11-15T09:02:08.887Z Has data issue: false hasContentIssue false
  • English
  • Français

Using One Strain Gauge Bridge to Detect Gait Events for a Robotic Prosthesis

Published online by Cambridge University Press:  01 April 2019

Yanggang Feng and
Qining WangOpen the ORCID record for Qining Wang[Opens in a new window]
Yanggang Feng
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, ChinaBeijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing 100871, China
Qining Wang*
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, ChinaBeijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing 100871, ChinaBeijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China
*
*Corresponding author. E-mail: qiningwang@pku.edu.cn
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

For a real-time robotic prosthetic control, gait event detection plays an important role. In this paper, one novel sensor was proposed to realize gait event detection. The sensor includes one strain gauge bridge, which can reflect the entire deformation of carbon-fiber footplate on a robotic prosthesis. Three unilateral transtibial amputees participated in the experiments. Experimental results show that using the proposed sensor method, gait event detection (stance phase and swing phase) accuracy is approximately 100%. Based on the detected gait events, three locomotion modes (sit, stand, and walk) and the corresponding transition modes could be determined. Difference between different gait event detection systems was further conducted.

Keywords

Man–Machine SystemsMechatronic SystemsSensor or Actuator DesignProsthesisHuman Biomechanics

Information

Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019, pp. 1987 - 1997
Copyright
© Cambridge University Press 2019 

References

Winter, D. A. and Sienko, S. E., “Biomechanics of below-knee amputee gait,” J. Biomech. 21(5), 361–367 (1988).CrossRefGoogle ScholarPubMed
Molen, N. H., “Energy/speed relation of below-knee amputees walking on motor-driven treadmill,” Int. Z Angew. Physiol. 31, 173–185 (1973).Google Scholar
Zhu, J., Wang, Q. and Wang, L., “On the design of a powered transtibial prosthesis with stiffness adaptable ankle and toe joints,” IEEE Trans. Ind. Electron. 61(9), 4797–4807 (2014).CrossRefGoogle Scholar
Klute, G. K., Czerniecki, J. M. and Hannaford, B., “Artificial muscles: Actuators for biorobotic systems,” Int. J. Robot. Res. 21(4), 295–309 (2002).CrossRefGoogle Scholar
Hitt, J., Sugar, T., Holgate, M., Bellman, R. and Hollander, K., “Robotic transtibial prosthesis with biomechanical energy regeneration,” Ind. Rob. 36(5), 441–447 (2009).CrossRefGoogle Scholar
Sup, F., Varol, H. A., Mitchell, J., Withrow, T. J. and Goldfarb, M., “Preliminary evaluations of a self-contained anthropomorphic transfemoral prosthesis,” IEEE ASME Trans. Mechatron. 14(6), 667–676 (2009).CrossRefGoogle ScholarPubMed
Au, S. K., Weber, J. and Herr, H., “Powered ankle-foot prosthesis improves walking metabolic economy,” IEEE Trans. Robot. 25(1), 51–66 (2009).CrossRefGoogle Scholar
Herr, H. and Grabowski, A. M., “Bionic ankle-foot prosthesis normalizes walking gait for persons with leg amputation,” Proc. Biol. Sci. 279, 457–464 (2012).CrossRefGoogle ScholarPubMed
Cherelle, P., Grosu, V., Matthys, A., Vanderborght, B. and Lefeber, D., “Design and validation of the ankle mimicking prosthetic (AMP-) foot 2.0,” IEEE Trans. Neural. Syst. Rehabil. Eng. 22(1), 138–148 (2014).CrossRefGoogle ScholarPubMed
Lawson, B. E., Mitchell, J., Truex, D., Shultz, A., Ledoux, E. and Goldfarb, M., “A robotic leg prosthesis: Design, control, and implementation,” IEEE Robot. Autom. Mag. 21, 70–81 (2014).CrossRefGoogle Scholar
Wang, Q., Yuan, K., Zhu, J. and Wang, L., “Walk the walk: A lightweight active transtibial prosthesis,” IEEE Robot. Autom. Mag. 22(4), 80–89 (2015).CrossRefGoogle Scholar
Feng, Y. and Wang, Q., “Combining push-off power and nonlinear damping behaviors for a lightweight motor-driven transtibial prosthesis,” IEEE ASME Trans. Mechatron. 22(6), 2512–2523 (2017).CrossRefGoogle Scholar
Zheng, E., Manca, S., Yan, T., Parri, A., Vitiello, N. and Wang, Q., “Gait phase estimation based on noncontact capacitive sensing and adaptive oscillators,” IEEE Trans. Biomed. Eng. 64(10), 2419–2430 (2017).CrossRefGoogle ScholarPubMed
Kyoungchul, K. and Tomizuka, M., “A gait monitoring system based on air pressure sensors embedded in a shoe,” IEEE ASME Trans. Mechatron. 14(3), 358–370 (2009).CrossRefGoogle Scholar
Mannini, A., Genovese, V. and Sabatini, A. M., “Online decoding of hidden Markov models for gait event detection using foot-mounted gyroscopes,” IEEE J. Biomed. Health Inform. 18(4), 1122–1130 (2014).CrossRefGoogle ScholarPubMed
Chen, B., Wang, Q. and Wang, L., “Adaptive slope walking with a robotic transtibial prosthesis based on volitional EMG control,” IEEE ASME Trans. Mechatron. 20(5), 2146–2157 (2015).CrossRefGoogle Scholar
Yuan, K., Wang, Q. and Wang, L., “Energy-efficient braking torque control of robotic transtibial prosthesis,” IEEE ASME Trans. Mechatron. 22(1), 149–160 (2017).CrossRefGoogle Scholar
Zheng, E. and Wang, Q., “Noncontact capacitive sensing-based locomotion transition recognition for amputees with robotic transtibial prostheses,” IEEE Trans. Neural. Syst. Rehabil. Eng. 25(2), 161–170 (2017).CrossRefGoogle ScholarPubMed
Özyürek, S., Demirbüken, Ï. and Angn, S., “Altered movement strategies in sit-to-stand task in persons with transtibial amputation,” Prosthet. Orthot. Int. 4(38), 303–309 (2014).CrossRefGoogle Scholar
Riener, R., Rabuffetti, M. and Frigo, C., “Stair ascent and descent at different inclinations,” Gait Posture 15, 32–44 (2002).CrossRefGoogle ScholarPubMed
Lay, A. N., Hass, C. J. and Gregor, R. J., “The effects of sloped surfaces on locomotion: A kinematic and kinetic analysis,” J. Biomech. 39, 1621–1628 (2006).CrossRefGoogle ScholarPubMed
Samantha, M. R., Graham, R. B. and Costigan, P. A., “Differentiation of young and older adult stair climbing gait using principal component analysis,” Gait Posture 31, 197–203 (2010).Google Scholar
Taborri, J., Palermo, E., Rossi, S. and Cappa, P., “Gait partitioning methods: A systematic review,” Sensors 16(1), 66 (2016).CrossRefGoogle ScholarPubMed