Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults

TL;DR

This paper presents a quantum hybrid support vector machine approach for stress detection in older adults, leveraging wearable sensor data and quantum computing to improve accuracy and recall in anomaly detection.

Contribution

It introduces a novel quantum machine learning technique for stress detection as an anomaly detection problem using wearable sensors and cortisol levels.

Findings

Quantum SVM improves accuracy over classical methods.

Quantum approach yields higher recall, reducing missed stress cases.

Method validated on 40 older adults with positive results.

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.