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Machine Learning Based Stress Monitoring in Older Adults Using Wearable Sensors and Cortisol as Stress Biomarker

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Abstract

The objective of this work is to evaluate the effectiveness of a wearable physiological stress monitoring system in distinguishing between stressed and non-stressed state in older adults using machine learning techniques. This system utilizes EDA and BVP signal to detect occurrence of stress as indicated by salivary cortisol measurement which is a reliable objective measure of physiological stress. Data of 19 healthy older adults (11 female and 8 male) with mean age 73.15 ± 5.79 were used for this study. EDA and BVP signals were recorded using a finger tip sensor during the Trier Social Stress Test, which is a well known experimental protocol to reliably induce stress in humans in a social setting. 39 statistical measures of the peak characteristic of EDA and BVP signal were extracted. A supervised feature selection algorithm is used to select important features as an input to the machine learning model. Four machine learning algorithms were evaluated based on their performance in classifying between stressed and non-stressed states. Results indicate that the logistic regression performed the best among Random Forest, κ-NN, and Support Vector Machine achieving an macro-average and micro-average f1-score of 0.87 and 0.95 respectively and an AUC score of 0.81. We also evaluated the effectiveness of a novel deep learning Long Short-Term Memory (LSTM) based classifier in distinguishing between stressed and non-stressed state. Results on test data shows that LSTM based classifier achieved an improvement of 6.7% and 2% in terms of macro-average f1-score and micro-average f1-score respectively. Also the AUC score for LSTM classifier is found to be 0.9 which is about 11% higher than the best performing logistic regression model. This work can be used to design a convenient unobtrusive wearable device to monitor stress levels in older adults in their home environment, thereby facilitating aging in place and improving the quality of life.

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Acknowledgments

This work was supported by the Kentucky Science and Engineering Foundation under Grant KSEF-3528-RDE-019.

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Authors and Affiliations

  1. VLSI Emerging Design And Nano Things Security Lab (VEDANTS-Lab), Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, USA

    Rajdeep Kumar Nath & Himanshu Thapliyal

  2. The Maya Angelou Center for Health Equity, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Allison Caban-Holt

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  1. Rajdeep Kumar Nath
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Correspondence to Himanshu Thapliyal.

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Appendix: Background on LSTM

Appendix: Background on LSTM

Figure 6
figure 6

LSTM cell structure used in this work.

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A single LSTM cell consists of three inputs: (i) Input at current time step, xt, (ii) Hidden state at previous time step, ht− 1 and (iii) Cell state at the previous time step, ct− 1. These inputs undergo a series of mathematical operations to give the cell state at the current time step, ct and the hidden state of the current time step, ht.

The cell state and hidden state at present time is estimated using the following steps:

  • First ht− 1 and xt is combined and passed through a sigmoid function. This is also known as forget gate. Sigmoid function bounds the output in between 0 and 1. A value of 0 means that particular information needs to be forgotten while values close to 1 indicates the high importance of the information. The output of the forget gate (ft) is then subjected to a point wise multiplication with ct− 1.

  • The output (It) of the sigmoid operation on xt + ht− 1 is multiplied point wise with the output (\(c^{\prime }_{t}\)) to generate the expression \(I_{t}+c^{\prime }_{t}\).

  • The present cell state is then given by the expression \(c_{t}=c_{t-1}*f_{t}+I_{t}*c^{\prime }_{t}\)

  • ht is calculated by performing point wise multiplication on the output from the sigmoid operation on xt + ht− 1 and ct.

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Nath, R.K., Thapliyal, H. & Caban-Holt, A. Machine Learning Based Stress Monitoring in Older Adults Using Wearable Sensors and Cortisol as Stress Biomarker. J Sign Process Syst 94, 513–525 (2022). https://doi.org/10.1007/s11265-020-01611-5

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