@inproceedings{4e40549ebddb429895e33dc7d4d8ebac,
title = "Demo: P-Fall: personalization pipeline for fall detection",
abstract = "We present an edge-cloud collaborative personalized fall detection pipeline called P-Fall. A personalized fall detection model requires real-time adaptation of a pre-trained model using real-time feedback data provided by the user. We, herein, highlight the design of the software architecture for a collaborative framework, the smart-watch's UI for the ease of collecting a user's feedback data, and the automation of the personalization process.",
keywords = "Fall detection, personalization of ML models, edge computing",
author = "Ngu, {Anne H.} and Awatif Yasmin and Tarek Mahmud and Adnan Mahmood and Sheng, {Quan Z.}",
year = "2023",
language = "English",
isbn = "9798350343960",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "173--174",
booktitle = "2023 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies CHASE 2023",
address = "United States",
note = "8th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2023 ; Conference date: 21-06-2023 Through 23-06-2023",
}