Weakly-supervised Learning of Human Dynamics

TL;DR

This paper introduces a weakly-supervised neural network framework for estimating human motion dynamics, enabling effective learning from small datasets without requiring ground truth forces or moments, and achieving state-of-the-art results.

Contribution

It presents a novel neural network architecture with specialized layers for dynamics estimation, allowing weakly-supervised learning and domain transfer from motion data.

Findings

Achieves state-of-the-art accuracy in force and torque regression

Maintains performance on reduced data sets

Enables learning without ground truth dynamics data

Abstract

This paper proposes a weakly-supervised learning framework for dynamics estimation from human motion. Although there are many solutions to capture pure human motion readily available, their data is not sufficient to analyze quality and efficiency of movements. Instead, the forces and moments driving human motion (the dynamics) need to be considered. Since recording dynamics is a laborious task that requires expensive sensors and complex, time-consuming optimization, dynamics data sets are small compared to human motion data sets and are rarely made public. The proposed approach takes advantage of easily obtainable motion data which enables weakly-supervised learning on small dynamics sets and weakly-supervised domain transfer. Our method includes novel neural network (NN) layers for forward and inverse dynamics during end-to-end training. On this basis, a cyclic loss between pure motion data can be minimized, i.e. no ground truth forces and moments are required during training. The proposed method achieves state-of-the-art results in terms of ground reaction force, ground reaction moment and joint torque regression and is able to maintain good performance on substantially reduced sets.