MPER – A motion profiling experiment and research system for human body movement
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed
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 language | English |
---|---|
Title of host publication | 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022 |
Pages | 88-90 |
Number of pages | 3 |
ISBN (Electronic) | 9781665416474 |
Publication status | Published - 23 Mar 2022 |
Peer-reviewed | No |
External IDs
Scopus | 85130619919 |
---|---|
dblp | conf/percom/RettlingerKWIHN22 |
Mendeley | db52aaec-4e9b-308d-a8b3-baafbd584236 |
unpaywall | 10.1109/percomworkshops53856.2022.9767484 |
ORCID | /0000-0001-7008-1537/work/142248636 |
Keywords
ASJC Scopus subject areas
Keywords
- bioinformatics, pervasive computing, sensor system, smart device