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Title: Unsupervised phase learning and extraction from repetitive movements
Authors: Jatesiktat, Prayook
Anopas, Dollaporn
Ang, Wei Tech
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Jatesiktat, P., Anopas, D., & Ang, W. T. (2018). Unsupervised phase learning and extraction from repetitive movements. Proceedings of 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 227-230. doi:10.1109/embc.2018.8512196
Abstract: Phase extraction from repetitive movements is one crucial part in various applications such as interactive robotics, physical rehabilitation, or gait analysis. However, pre-existing automatic phase extraction techniques are specific to a target movement due to some handcrafted-features. To make it more universal, a novel unsupervised-learning-based phase extraction technique is proposed. A neural network architecture and a cost function are designed to learn the concept of phase from records of a repetitive movement without any given phase label. The method is tested on a rat's gait cycle and a human's upper limb movement. The phases are successfully extracted at the sample level despite the variations in movement speed, trajectory, or subject's anthropometric features.
ISBN: 9781538636466
DOI: 10.1109/EMBC.2018.8512196
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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