IoT Based Fall Detection System for Elderly Healthcare

Author:

Tahir AhsenORCID,Taylor WilliamORCID,Taha AhmadORCID,Usman MuhammadORCID,Shah Syed AzizORCID,Imran Muhammad AliORCID,Abbasi Qammer H.ORCID

Publisher

Springer Nature Singapore

Reference58 articles.

1. World Health Organization: Falls. https://www.who.int/news-room/fact-sheets/detail/falls (2021). Accessed 1 July 2021

2. PCP market research: falls, measuring the impact on older people (2012)

3. Tian Y., Thompson J., Buck D., Sonola L.: Exploring the system-wide costs of falls in older people in Torbay. King’s Fund (2013)

4. Noury N., Rumeau P., Bourke A.K., ÓLaighin G., Lundy J.E.: A proposal for the classification and evaluation of fall detectors. Innovation Res. Biomed. Eng. 29(6), 340–349 (2008)

5. Tahir, A., Ahmad, J., Morison, G., Larijani, H., Gibson, R., Skelton, D.: HRNN4f: Hybrid deep random neural network for multi-channel fall activity detection. Probab. Eng. Informational Sci. 1–14 (2019)

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4. Real-Time Fall Detection using ESP32 and AMG8833 Thermal Sensor: A Non-Wearable Approach for Enhanced Safety;2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS);2023-08-23

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