Quantum machine learning for intelligent health monitoring and prediction in wearable systems

Abstract

The rapid development and integration of wearable technologies or Internet-of-Things (IoT) into healthcare technologies with computational methods holds transformative potential for future healthcare systems, especially for personalized health monitoring and early disease detection. In parallel, recent advancements in quantum computing have o↵ered significant interest in quantum machine learning (QML), an intersection of quantum computing and machine learning, which provides a novel hybrid computational paradigm capable of addressing complex healthcare challenges that remain unsolvable with classical machine learning techniques. This thesis explores the application of quantum-enhanced recurrent neural network architectures, specifically Quantum Long Short-Term Memory (QLSTM) and Quantum-based Gated Recurrent Unit (QGRU), for predictive modeling and activity classification in wearable IoT systems, focusing particularly on their applicability and efficacy to elderly populations. One of the key challenges in modern healthcare intelligence systems is the simple but accurate estimation mechanism of Physical Activity Energy Expenditure (PAEE), which is an essential factor for assessing physical health status, particularly for healthy aging. In order to address this challenge, the first work proposes a model called an enhanced QLSTM with linear layer (eQLSTML) that integrates Variational Quantum Circuits (VQCs) into classical LSTM architectures. This integration notably explores the advantages of quantum computing, including properties such as entanglement and superposition, to enhance the model’s ability to capture complex temporal dependencies and subtle variations in human activity patterns. The modelling experiments were evaluated using the publicly available GOTOV Human Physical Activity dataset, which includes accelerometer data collected from older adults engaged in various daily activities. In addition to modelling architecture analysis, this thesis provides comprehensive analyses concerning the scalability, feasibility, and computational complexity of the proposed quantum-enhanced models compared to the classical backbone. The overall results demonstrate that the proposed quantum-enhanced approach significantly outperforms traditional classical machine learning algorithms, which demonstrates the potential ability for accurate predictions and robustness to noisy data like accelerometer data. In addition, accurate classification of activities of daily living (ADLs) is another critical aspect for early disease detection and e↵ective healthcare intervention. Within the context of this thesis, the second work introduces a novel hybrid model called QGRU-Multiclass Classifier (QGRU-MC). The data preprocessing involves creating a statistical feature extraction and applying oversampling techniques – SMOTE-ENN (Synthetic Minority Oversampling Technique with Edited Nearest Neighbors) for the publicly available dataset called “Dataset for ADL recognition with Wrist-worn Accelerometer”. The preliminary findings suggest that our model has good potential for healthcare applications, particularly in advancing future intelligent systems focused on daily activity monitoring. Specifically, QGRU-MC achieved significant improvements in classification accuracy, sensitivity, and specificity across multiple categories of daily activities, highlighting its potential for practical deployment in intelligent wearable systems aimed at monitoring and supporting healthy aging. Furthermore, this work also discusses the future research directions and practical considerations for transforming from the simulators to modern real quantum computers in the noisy intermediate-scale quantum (NISQ) era for a more comprehensive view on exploiting potential impacts on public health policies and personalized healthcare systems. Overall, simulator-based results indicate promising potential for integrating QML methods into wearable healthcare applications such as predictive analytics, personalized treatments, and health disease intervention to improve healthcare outcomes. Specifically, on PAEE regression, the proposed eQLSTML improved R² by 13.2% and reduced RMSE and MAE by 14.8% and 19.1%, respectively, relative to the classical LSTM; on ADL, QGRU-MC demonstrates good macro-averaged metrics across all eight classes (see Chs. 3–4). This research contributes significantly to the growing body of literature supporting quantum computing technologies in healthcare applications, making QML an incredible tool for addressing complex healthrelated computational challenges in the era of big and complex data in medical fields and smart healthcare technologies, especially in e↵ectively solving data from multiple sources, wearable accelerometry, and multi-class ADL recognition with complicated tasks.