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Authors: Eduarda Vaz 1 ; Heitor Cardoso 2 and Plinio Moreno 2

Affiliations: 1 Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal ; 2 Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, Torre Norte Piso 7, 1049-001 Lisboa, Portugal

Keyword(s): Fall Detection, Wrist Devices, Sensor Simulation, Unity Environment.

Abstract: Realistic fall detection datasets are difficult to acquire due to the high risks, awkward situation of pretending to be falling and limited to young healthy individuals. In this work we propose to leverage on motion capture data acquired for games and animations, to simulate the recordings of accelerometers and orientation sensors. The simulated sensor values are obtained in the Unity environment. Our dataset allows to further evaluate the generalization properties of previously presented methods by including new types of both falling and non-falling samples. Our case study is the fall detection based on wristband devices.

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Paper citation in several formats:
Vaz, E.; Cardoso, H. and Moreno, P. (2022). Evaluation of Fall Detection Approaches based on Virtual Devices: Leveraging on Motion Capture Data in Unity environments. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 50-56. DOI: 10.5220/0010843600003123

@conference{biosignals22,
author={Eduarda Vaz. and Heitor Cardoso. and Plinio Moreno.},
title={Evaluation of Fall Detection Approaches based on Virtual Devices: Leveraging on Motion Capture Data in Unity environments},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS},
year={2022},
pages={50-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010843600003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS
TI - Evaluation of Fall Detection Approaches based on Virtual Devices: Leveraging on Motion Capture Data in Unity environments
SN - 978-989-758-552-4
IS - 2184-4305
AU - Vaz, E.
AU - Cardoso, H.
AU - Moreno, P.
PY - 2022
SP - 50
EP - 56
DO - 10.5220/0010843600003123
PB - SciTePress

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