Online Phase Estimation of Human Oscillatory Motions using Deep Learning

TL;DR

This paper presents a deep learning method using LSTM networks for real-time phase estimation of human motion, improving synchronization in oscillatory systems and aiding motion analysis.

Contribution

It introduces a novel online phase estimation approach with a calibration procedure for spatial invariance, applied to human motion and oscillator synchronization.

Findings

Effective phase estimation on motion capture data

Improved synchronization in Kuramoto oscillator network

Demonstrated robustness to spatial variations

Abstract

Accurately estimating the phase of oscillatory systems is essential for analyzing cyclic activities such as repetitive gestures in human motion. In this work we introduce a learning-based approach for online phase estimation in three-dimensional motion trajectories, using a Long Short- Term Memory (LSTM) network. A calibration procedure is applied to standardize trajectory position and orientation, ensuring invariance to spatial variations. The proposed model is evaluated on motion capture data and further tested in a dynamical system, where the estimated phase is used as input to a reinforcement learning (RL)-based control to assess its impact on the synchronization of a network of Kuramoto oscillators.