Machine Learning Based Anxiety Detection in Older Adults using Wristband Sensors and Context Feature

TL;DR

This study presents a machine learning approach using wristband sensors and context features to detect anxiety in older adults, demonstrating improved accuracy and real-time monitoring potential with low-cost devices.

Contribution

It introduces a novel combination of physiological signals and context features for anxiety detection in older adults using wristband sensors, improving accuracy over physiological data alone.

Findings

Combining physiological and context features increased detection accuracy by up to 6.41%.

The method enables real-time anxiety monitoring with low-cost wristband sensors.

The approach is validated on a year-long dataset from 41 older adults.

Abstract

This paper explores a novel method for anxiety detection in older adults using simple wristband sensors such as Electrodermal Activity (EDA) and Photoplethysmogram (PPG) and a context-based feature. The proposed method for anxiety detection combines features from a single physiological signal with an experimental context-based feature to improve the performance of the anxiety detection model. The experimental data for this work is obtained from a year-long experiment on 41 healthy older adults (26 females and 15 males) in the age range 60-80 with mean age 73.36+-5.25 during a Trier Social Stress Test (TSST) protocol. The anxiety level ground truth was obtained from State-Trait Anxiety Inventory (STAI), which is regarded as the gold standard to measure perceived anxiety. EDA and Blood Volume Pulse (BVP) signals were recorded using a wrist-worn EDA and PPG sensor respectively. 47 features were computed from EDA and BVP signal, out of which a final set of 24 significantly correlated features were selected for analysis. The phases of the experimental study are encoded as unique integers to generate the context feature vector. A combination of features from a single sensor with the context feature vector is used for training a machine learning model to distinguish between anxious and not-anxious states. Results and analysis showed that the EDA and BVP machine learning models that combined the context feature along with the physiological features achieved 3.37% and 6.41% higher accuracy respectively than the models that used only physiological features. Further, end-to-end processing of EDA and BVP signals was simulated for real-time anxiety level detection. This work demonstrates the practicality of the proposed anxiety detection method in facilitating long-term monitoring of anxiety in older adults using low-cost consumer devices.