MPER – A motion profiling experiment and research system for human body movement

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributed

Contributors

Abstract

State-of-the-art approaches in gait analysis usually rely on one isolated tracking system, generating insufficient data for complex use cases such as sports, rehabilitation, and MedTech. We address the opportunity to comprehensively understand human motion by a novel data model combining several motion-tracking methods. The model aggregates pose estimation by captured videos and EMG and EIT sensor data synchronously to gain insights into muscle activities. Our demonstration with biceps curl and sitting/standing pose generates time-synchronous data and delivers insights into our experiment's usability, advantages, and challenges.

Details

Original languageEnglish
Title of host publication2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022
Pages88-90
Number of pages3
ISBN (Electronic)9781665416474
Publication statusPublished - 23 Mar 2022
Peer-reviewedNo

External IDs

Scopus 85130619919
dblp conf/percom/RettlingerKWIHN22
Mendeley db52aaec-4e9b-308d-a8b3-baafbd584236
unpaywall 10.1109/percomworkshops53856.2022.9767484