hoangNguyen210/Fall-Detection-Research-1

This is a brief summary of our Fall detection research 1. Our work have been accepted to present at MMM 2022 Conference (6-10 June 2022).

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Experimental

This project helps develop and evaluate systems for automatically detecting falls in elderly individuals. It takes in multimodal data from sensors (wearable and ambient) and cameras to identify fall events. The results show how different machine learning and deep learning models perform in accurately recognizing falls, benefiting healthcare providers or caretakers looking to implement proactive safety measures.

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Use this if you are a researcher or care provider interested in understanding the effectiveness of various sensor and vision-based systems for automatic fall detection among the elderly.

Not ideal if you are looking for a ready-to-deploy, plug-and-play fall detection product for immediate home installation.

elderly-care fall-prevention remote-monitoring assisted-living gerontology
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Last pushed

Apr 09, 2022

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