Quantum-based Gated Recurrent Units for Multiclass Classification to Monitor Daily Living Activities for Early Disease Detection

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 wear-able 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.