Discrimination of simultaneous psychological and physical stressors using wristband biosignals

Abstract

Background and objective: In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provide APS and PA information in real-time management of chronic conditions such as diabetes by automated personalized insulin delivery. Monitoring APS noninvasively throughout free-living conditions remains challenging because the responses to APS and PA of many physiological variables measured by wearable devices are similar.

Methods: Various classification algorithms are compared to simultaneously detect and discriminate the PA (sedentary state, treadmill running, and stationary bike) and the type of APS (non-stress state, mental stress, and emotional anxiety). The impact of APS inducements is verified with commonly used self-reported questionnaires (The State-Trait Anxiety Inventory (STAI)). To aid the classification algorithms, novel features are generated from the physiological variables reported by a wristband device during 117 hours of experiments involving simultaneous APS inducement and PA. We also translate the APS assessment into a quantitative metric for use in predicting the adverse outcomes.

Results: An accurate classification of the concurrent PA and APS states is achieved with an overall classification accuracy of 99% for PA and 92% for APS. The average accuracy of APS detection during sedentary state, treadmill running, and stationary bike is 97.3, 94.1, and 84.5%, respectively.

Conclusions: The simultaneous assessment of APS and PA throughout free-living conditions from a convenient wristband device is useful for monitoring the factors contributing to an elevated risk of acute events in people with chronic diseases like cardiovascular complications and diabetes.

Keywords: Acute psychological stress; Discrimination of physical and psychological stressors; Machine learning; Physical activity; Wearable devices.

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no conflicts of interest.

Copyright © 2020. Published by Elsevier B.V.

Figures

Figure B.10:

Overview of PPG Signal Processing

Figure B.11:

Raw Data Frequency Domain Analysis

Figure B.12:

Bandpass Filtered BVP Data Frequency Domain Analysis

Figure B.13:

Time Domain Analysis - After Bandpass Filter Design

Figure B.14:

Noise Cancellation with NRLS Adaptive Filter

Figure B.15:

Improvement in the BVP data from the proposed signal processing approach to remove noise and motion artifacts. (top: raw PPG data and the bioPlux data; bottom: processed PPG data and the bioPlus data)

Figure B.16:

Frequency domain analysis of the improvement in the BVP data from the proposed signal processing approach to remove noise and motion artifacts. (top: raw PPG data and the bioPlux data; bottom: processed PPG data and the bioPlus data)

Figure 1:

Overview of Proposed Method (Stress Level: Normalized APS Estimates [0–1]), (Numbers: Percentage Classification Accuracy (Testing) for Each Individual Branch)

Figure 2:

Signals for Discriminating Exercise from APS (Red and italics) Can Measured by Empatica E4 [, , , –22]

Figure 3:

Energy Expenditure Measurements for Each Cases (TR: Treadmill, NS: Non-Stress, EAS: Exciting-Anxiety Stress, MS: Mental-Stress, BK: Bike Exercise)

Figure 4:

Example Processed and Raw BVP Signal (Details In: Appendix B)

Figure 5:

Data Preparation and Separation (D: Downsampling with k-means Clustering, U: Upsampling with Adaptive Synthetic Sampling (ADASYN) (Maximum Upsampling Rate: 25%), NS: Non-Stress, EAS: Exciting-Anxiety Stress, MS: Mental Stress, SS: Sedentary State, TR: Treadmill, SB: Stationary Bike, PS: Physical State, APS: Acute Psychological Stress)

Figure 6:

Comparison of Accuracy of PS Classification with Different Algorithms

Figure 7:

Confusion Matrices of PS and APS Classification Algorithms (10% Testing Data Set)

Figure 8:

APS Level Estimation During Different Physical Activities

Figure 9:

Classification of Different APS by Various ML Algorithms During Different Physical Activities