Automated remote fall detection using impact features from video and audio-Reference-Cited by-同舟云学术

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Automated remote fall detection using impact features from video and audio

Published:2019-05 Issue: Volume:88 Page:25-32
ISSN:0021-9290
Container-title:Journal of Biomechanics
language:en
Short-container-title:Journal of Biomechanics

Author:

Geertsema Evelien E.^ORCID,Visser Gerhard H.^ORCID,Viergever Max A.,Kalitzin Stiliyan N.^ORCID

Funder

Margaret Knip Fund

Christelijke Vereniging voor de Verpleging van Lijders aan Epilepsie

ZonMW

Publisher

Elsevier BV

Subject

Rehabilitation,Biomedical Engineering,Orthopedics and Sports Medicine,Biophysics

Reference48 articles.

1. The implementation of an intelligent and video-based fall detection system using a neural network;Alhimale;Appl. Soft Comput.,2014

2. Belshaw, M., Taati, B., Snoek, J., Mihailidis, A., 2011. Towards a single sensor passive solution for automated fall detection. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBS, pp. 1773–1776. 10.1109/IEMBS.2011.6090506.

3. Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification;Charfi;J. Electron. Imaging,2013

4. Home camera-based fall detection system for the elderly;De Miguel;Sensors (Switzerland),2017

5. Debard, G., Baldewijns, G., Goedem, T., Tuytelaars, T., Vanrumste, B., 2015. Camera-based fall detection using a particle filter. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6947–6950. 10.1109/EMBC.2015.7319990.

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