Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator

Yoram Segal, Ofer Hadar, Lenka Lhotska

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.

Original languageEnglish
Title of host publicationpHealth 2022 - Proceedings of the 19th International Conference on Wearable Micro and Nano Technologies for Personalized Health
EditorsBernd Blobel, Bian Yang, Mauro Giacomini
PublisherIOS Press BV
Pages97-103
Number of pages7
ISBN (Electronic)9781643683485
DOIs
StatePublished - 3 Nov 2022
Externally publishedYes
Event19th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2022 - Oslo, Norway
Duration: 8 Nov 202210 Nov 2022

Publication series

NameStudies in Health Technology and Informatics
Volume299
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference19th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2022
Country/TerritoryNorway
CityOslo
Period8/11/2210/11/22

Bibliographical note

Publisher Copyright:
© 2022 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)

Keywords

  • Generative Adversarial Network (GAN)
  • Human body movements
  • OpenPose
  • Rehabilitation
  • Siamese twins Neural Network
  • Simulator

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