Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors

Abstract

Abstract-Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects.Index Terms-Electrodermal activity, heart rate variability, mental stress, wearable sensors.

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Wireless Sensor Network Multi-Hop Positioning Algorithm Based on Continuous Regression

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“…value is shown in Table 1: Figure 2. Cluster heads candidate regions In order to balance the energy consumption of nodes, when the average residual energy of the candidate nodes in the parent grid is less than 50%, the parent grid will be split into four sub-grids of equal areas, which is shown in [1,0], [1,1]. The nodes in the sub-grids will be calculated to judge which one they belong to according to the equation (5) …”

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