Demo: P-Fall: personalization pipeline for fall detection

Anne H. Ngu, Awatif Yasmin, Tarek Mahmud, Adnan Mahmood, Quan Z. Sheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies CHASE 2023
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages173-174
Number of pages2
ISBN (Electronic)9798400701023
ISBN (Print)9798350343960
Publication statusPublished - 2023
Event8th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2023 - Orlando, United States
Duration: 21 Jun 202323 Jun 2023

Publication series

Name
ISSN (Print)2832-2967
ISSN (Electronic)2832-2975

Conference

Conference8th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2023
Country/TerritoryUnited States
CityOrlando
Period21/06/2323/06/23

Keywords

  • Fall detection
  • personalization of ML models
  • edge computing

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