Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network-Reference-Cited by-同舟云学术

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Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network

Published:2022 Issue: Volume: Page:216-224
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Mekruksavanich Sakorn,Jantawong Ponnipa,Hnoohom Narit,Jitpattanakul Anuchit

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-20992-5_19

Reference21 articles.

1. Colón, L.N.V., DeLaHoz, Y., Labrador, M.: Human fall detection with smartphones. In: 2014 IEEE Latin-America Conference on Communications (LATINCOM), pp. 1–7 (2014). https://doi.org/10.1109/LATINCOM.2014.7041879

2. Hsieh, S.L., Yang, C.T., Li, H.J.: Combining wristband-type devices and smartphones to detect falls. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2373–2377 (2017). https://doi.org/10.1109/SMC.2017.8122977

3. Klenk, J., et al.: The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls. Eur. Rev. Aging Phys. Act. 13, 8 (2016). https://doi.org/10.1186/s11556-016-0168-9

4. Mannini, A., Trojaniello, D., Cereatti, A., Sabatini, A.M.: A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and huntington’s disease patients. Sensors 16(1), 134 (2016). https://doi.org/10.3390/s16010134

5. Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., Peñafort-Asturiano, C.: UP-fall detection dataset: a multimodal approach. Sensors, 19(9) (2019). https://doi.org/10.3390/s19091988

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3. Classifying Activities of Electrical Line Workers Based on Deep Learning Approaches Using Wrist-Worn Sensor;2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE);2023-06-28

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