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Video-Audio Multimodal Fall Detection Method
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-9464-7010
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0998-6585
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-3797-4605
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
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2025 (English)In: PRICAI 2024: Trends in Artificial Intelligence: 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18–24, 2024, Proceedings, Part IV / [ed] Rafik Hadfi; Patricia Anthony; Alok Sharma; Takayuki Ito; Quan Bai, Springer, 2025, p. 62-75Conference paper, Published paper (Refereed)
Abstract [en]

Falls frequently present substantial safety hazards to those who are alone, particularly the elderly. Deploying a rapid and proficient method for detecting falls is a highly effective approach to tackle this concealed peril. The majority of existing fall detection methods rely on either visual data or wearable devices, both of which have drawbacks. This research presents a multimodal approach that integrates video and audio modalities to address the issue of fall detection systems and enhances the accuracy of fall detection in challenging environmental conditions. This multimodal approach, which leverages the benefits of attention mechanism in both video and audio streams, utilizes features from both modalities through feature-level fusion to detect falls in unfavorable conditions where visual systems alone are unable to do so. We assessed the performance of our multimodal fall detection model using Le2i and UP-Fall datasets. Additionally, we compared our findings with other fall detection methods. The outstanding results of our multimodal model indicate its superior performance compared to single fall detection models.

Place, publisher, year, edition, pages
Springer, 2025. p. 62-75
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 15284
Keywords [en]
Audio classification, Fall detection, Multimodal, Video classification, Video analysis, Detection methods, Detection models, Effective approaches, Multi-modal, Multi-modal approach, Performance, Safety hazards
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:mau:diva-72628DOI: 10.1007/978-981-96-0125-7_6Scopus ID: 2-s2.0-85210317498ISBN: 978-981-96-0124-0 (print)ISBN: 978-981-96-0125-7 (electronic)OAI: oai:DiVA.org:mau-72628DiVA, id: diva2:1919887
Conference
21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18–24, 2024
Available from: 2024-12-10 Created: 2024-12-10 Last updated: 2025-02-07Bibliographically approved

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Jamali, MahtabDavidsson, PaulKhoshkangini, RezaMihailescu, Radu-Casian

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Jamali, MahtabDavidsson, PaulKhoshkangini, RezaMihailescu, Radu-Casian
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