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

Author:

Melo Nicholas B.,Dórea Carlos E. T.,Alsina Pablo J.,Araújo Márcio V.

Abstract

SUMMARYIn 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.

Publisher

Cambridge University Press (CUP)

Subject

Computer Science Applications,General Mathematics,Software,Control and Systems Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RoboWalk Trajectory Planning Based on the Human Gait Prediction Using LSTM;2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM);2021-11-17

2. Development of a Control System and Functional Validation of a Parallel Robot for Lower Limb Rehabilitation;Actuators;2021-10-18

3. Upper Extremity Load Reduction for Lower Limb Exoskeleton Trajectory Generation Using Ankle Torque Minimization;2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV);2020-12-13

4. Gait Trajectory and Gait Phase Prediction Based on an LSTM Network;Sensors;2020-12-12

5. Gait Trajectory and Event Prediction from State Estimation for Exoskeletons During Gait;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2020-01

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