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Bipedal gait model for precise gait recognition and optimal triggering in foot drop stimulator: a proof of concept

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Abstract

Electrical stimulators are often prescribed to correct foot drop walking. However, commercial foot drop stimulators trigger inappropriately under certain non-gait scenarios. Past researches addressed this limitation by defining stimulation control based on automaton of a gait cycle executed by foot drop of affected limb/foot only. Since gait is a collaborative activity of both feet, this research highlights the role of normal foot for robust gait detection and stimulation triggering. A novel bipedal gait model is proposed where gait cycle is realized as an automaton based on concurrent gait sub-phases (states) from each foot. The input for state transition is fused information from feet-worn pressure and inertial sensors. Thereafter, a bipedal gait model-based stimulation control algorithm is developed. As a feasibility study, bipedal gait model and stimulation control are evaluated in real-time simulation manner on normal and simulated foot drop gait measurements from 16 able-bodied participants with three speed variations, under inappropriate triggering scenarios and with foot drop rehabilitation exercises. Also, the stimulation control employed in commercial foot drop stimulators and single foot gait-based foot drop stimulators are compared alongside. Gait detection accuracy (98.9%) and precise triggering under all investigations prove bipedal gait model reliability. This infers that gait detection leveraging bipedal periodicity is a promising strategy to rectify prevalent stimulation triggering deficiencies in commercial foot drop stimulators.

Bipedal information-based gait recognition and stimulation triggering

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Acknowledgements

Authors pay thanks to Akshat Bisht from Electrical and Computer Engineering Department, University of Auckland for helping in the development of experimentation and data collection setups as well as to all the participants in this research.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Auckland, Auckland, 1010, New Zealand

    Muhammad Faraz Shaikh, Zoran Salcic, Kevin I-Kai Wang & Aiguo Patrick Hu

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  1. Muhammad Faraz Shaikh
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  2. Zoran Salcic
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  3. Kevin I-Kai Wang
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  4. Aiguo Patrick Hu
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Correspondence to Muhammad Faraz Shaikh.

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All procedures were performed in accordance with the ethical standards of the University of Auckland human participants and ethics approval committee (UAHPEC) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The authors declare that they have no conflict of interest.

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Shaikh, M.F., Salcic, Z., Wang, K.IK. et al. Bipedal gait model for precise gait recognition and optimal triggering in foot drop stimulator: a proof of concept. Med Biol Eng Comput 56, 1731–1746 (2018). https://doi.org/10.1007/s11517-018-1810-7

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  • DOI: https://doi.org/10.1007/s11517-018-1810-7

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