Wearables and Detection of Falls: A Comparison of Machine Learning Methods and Sensors Positioning
Author:
Funder
Fapesp
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s11063-021-10724-2.pdf
Reference29 articles.
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2. Saftari LN, Kwon O-S (2018) Ageing vision and falls: a review. J Physiol Anthropol. https://doi.org/10.1186/s40101-018-0170-1
3. Wang X, Ellul J, Azzopardi G (2020) Elderly fall detection systems: a literature survey. Front Robot AI 7:71. https://doi.org/10.3389/frobt.2020.00071
4. Villar JR, Chira C, de la Cal E, González VM, Sedano J, Khojasteh SB (2020) Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.12.147
5. Ozcan K, Velipasalar S, Varshney PK (2017) Autonomous fall detection with wearable cameras by using relative entropy distance measure. IEEE Trans Hum Mach Syst 47(1):31–39. https://doi.org/10.1109/THMS.2016.2620904
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