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Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors

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Abstract

This paper presents a method to regenerate lower limb joint angle trajectories during gait cycle by judging human intention using wearable sensor system. Myoelectric signals from user are used to detect the intention of gait initiation and gait phases. Multi-channel redundant fusion technique is implemented to obtain a robust stride time and gait phase calculation algorithm. Joint trajectories corresponding to particular gait events and phases are regenerated using a Radial basis neural network. The network is trained with joint angle data measured by Inertial Measurement Unit (IMU) from users with varying anthropomorphic features. Generated trajectory is adaptive to anthropomorphic as well as gait velocity variation. Contribution of this paper is in development of a wearable sensor system, multi-channel redundant fusion to calculate stride time and an adaptive gait trajectory generation algorithm. The proposed method of trajectory generation is used to regenerate lower limb joint motion in sagittal plane for wearable robotic devices like prosthesis and active lower limb exoskeleton.

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Authors and Affiliations

  1. School of Mechatronics and Robotics, Indian Institute of Engineering Science and Technology, Howrah, 711103, India

    Oishee Mazumder, Ananda Sankar Kundu & Subhasis Bhaumik

  2. National Institute of Orthopaedically Handicapped Bonhooghly, Kolkata, 700090, India

    Prasanna Kumar Lenka

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  1. Oishee Mazumder
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  2. Ananda Sankar Kundu
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  3. Prasanna Kumar Lenka
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  4. Subhasis Bhaumik
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Correspondence to Oishee Mazumder.

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Mazumder, O., Kundu, A.S., Lenka, P.K. et al. Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors. J Intell Robot Syst 86, 335–351 (2017). https://doi.org/10.1007/s10846-016-0436-y

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  • DOI: https://doi.org/10.1007/s10846-016-0436-y

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