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An Instance-Based Algorithm With Auxiliary Similarity Information for the Estimation of Gait Kinematics From Wearable Sensors

<24> Automatically capturing human movement data in real-world situations has wide applications for medical diagnoses and treatments. This paper is significant because it develops a novel general regression neural network and its adaptive learning algorithm for the estimation of gait kinematics. Empirical evaluation based on real noise data shows that the proposed approach provides robust and accurate estimation, and therefore it paves the way for real clinical applications.