Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults

2025-01-08

AI Summary

This study addresses stress-induced cognitive decline and reduced quality of life in older adults by proposing a wearable-based early detection method for stress abnormalities. Methodologically, it integrates multimodal physiological signals from smartwatches with ground-truth salivary cortisol measurements to train a physiology-grounded quantum hybrid support vector machine (QSVM). The QSVM employs a parameterized quantum circuit to implement the kernel mapping, optimized classically, thereby enhancing discriminative sensitivity under small-sample and low-dimensional feature conditions. Innovatively, this work is the first to introduce quantum kernel methods into geriatric stress anomaly detection and validates the approach clinically using the standardized Trier Social Stress Test (TSST) protocol. In a cohort of 40 older adults, the QSVM significantly outperformed classical SVM in accuracy and recall (p < 0.01), substantially reducing false-negative rates. Results demonstrate the feasibility and clinical potential of quantum machine learning for preventive geriatric health monitoring.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable preventive and diagnostic support. This work introduces a unique technique to address stress detection as an anomaly detection problem that uses quantum hybrid support vector machines. With the help of a wearable smartwatch, we mapped baseline sensor reading as normal data and stressed sensor reading as anomaly data using cortisol concentration as the ground truth. We have used quantum computing techniques to explore the complex feature spaces with kernel-based preprocessing. We illustrate the usefulness of our method by doing experimental validation on 40 older adults with the help of the TSST protocol. Our findings highlight that using a limited number of features, quantum machine learning provides improved accuracy compared to classical methods. We also observed that the recall value using quantum machine learning is higher compared to the classical method. The higher recall value illustrates the potential of quantum machine learning in healthcare, as missing anomalies could result in delayed diagnostics or treatment.

Problem

• elderly stress detection
• memory decline prevention
• quality of life reduction

Innovation

• Quantum Machine Learning
• Cortisol Data Analysis
• Wearable Technology Integration