Unsupervised phase learning and extraction from repetitive movements

• Main Authors: Jatesiktat, Prayook, Anopas, Dollaporn, Ang, Wei Tech
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Electrical and electronic engineering; Phase Extraction; Repetitive Movements
• Online Access: https://hdl.handle.net/10356/137348
• Institution: Nanyang Technological University

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.