AI-driven Human Motion Classification and Analysis using Laban Movement System

Table of Contents

1. Introduction
2. Problem Statement
3. Objective
4. Presentation video
5. Acknowledgements
6. Cite this paper

Introduction

Human movement study:

• Health sciences (biomechanics, kinesiology, neurology, sports medicine)
• Arts (theater and dance, cultural studies)
• Intersection (arts in medicine, dance therapy)

Multi-modal datasets:

• Video
• Skeletal motion capture
• Manual annotations
• Clinical metadata

Problem Statement

Traditional Laban movement analysis (LMA):

• 4-dimensional features (shape, effort, space, body)
• Manual input from professionals
• Time-consuming
• Human errors

Objective

Creating automated Laban movement annotation:

• Training four different machine learning algorithms through supervised learning on existing human motion datasets of video and skeletal sequences
• Test feature extraction methods (within and across frames) to improve the annotation accuracy
• Input raw videos and export Laban annotated videos

Check code file AI-HumanMotionAnalysis-Test.ipynb

Acknowledgements

This project was supported by the 2020 UF AI Catalyst grant from the UF Office of Research. The authors acknowledge the University of Florida HiPerGator Research Computing for providing computational resources and support that have contributed to the research results reported in this publication.

Presentation Video

AI Research Catalyst Fund Awardees Virtual Seminar Series
Presentation Video

Cite this paper

Full PDF file

Guo, Wenbin, Osubi Craig, Timothy Difato, James Oliverio, Markus Santoso, Jill Sonke, and Angelos Barmpoutis. (2022). AI-Driven Human Motion Classification and Analysis Using Laban Movement System. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Anthropometry, Human Behavior, and Communication. HCII 2022. Lecture Notes in Computer Science, vol 13319. Springer, Cham. https://doi.org/10.1007/978-3-031-05890-5_16