Quantum-based gated recurrent units for multiclass classification to monitor daily living activities for early disease detection

Bao-Nhi Dang Tran, Muhammad Fahim, Bradley D. E. McNiven, Stephen Czarnuch, Octavia A. Dobre, Trung Q. Duong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The continuous monitoring of activities of daily living (ADLs) can play a vital role in assessing an individuals capability to live independently and enable the possibility for early disease detection. This paper introduces a novel hybrid model, called quantum-based gated recurrent unit - multiclass classifier (QGRU-MC), to enhance ADL classification from wearable sensor data. Using statistical feature extraction from the raw accelerometer sensor signals, the QGRU-MC model demonstrates good performance in activity recognition. Preliminary findings suggest that our model has good potential in healthcare applications, and in particular, can contribute to the advancement of future intelligent systems centered on daily activity monitoring and the promotion of healthy aging.
Original languageEnglish
Title of host publication2024 IEEE International Conference on E-health Networking, Application & Services (Healthcom): Proceedings
PublisherIEEE Xplore
Publication statusAccepted - 30 Aug 2024

Keywords

  • Quantum-based gated recurrent units
  • gated recurrent units
  • multiclass classification
  • early disease detection

Fingerprint

Dive into the research topics of 'Quantum-based gated recurrent units for multiclass classification to monitor daily living activities for early disease detection'. Together they form a unique fingerprint.

Cite this