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MPER - a Motion Profiling Experiment and Research system for human body movement

Rettlinger, Sebastian; Knaus, Bastian; Wieczorek, Florian; Ivakko, Nikolas; Hanisch, Simon; Nguyen, Giang T.; Strufe, Thorsten ORCID iD icon; Fitzek, Frank H. P.

Abstract (englisch):

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.


Originalveröffentlichung
DOI: 10.1109/PerComWorkshops53856.2022.9767484
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Institut für Telematik (TM)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2022
Sprache Englisch
Identifikator ISBN: 978-1-6654-1648-1
KITopen-ID: 1000148616
HGF-Programm 46.23.01 (POF IV, LK 01) Methods for Engineering Secure Systems
Erschienen in 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Pisa, Italy, 21-25 March 2022
Veranstaltung IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM 2022), Pisa, Italien, 21.03.2022 – 25.03.2022
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 88-90
Schlagwörter sensor system, smart device, pervasive computing, bioinformatics
Nachgewiesen in Scopus
Dimensions
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