Stress Detection With Single PPG Sensor by Orchestrating Multiple Denoising and Peak-Detecting Methods

Heo, Seongsil; Kwon, Sunyoung; Lee, Jaekoo

doi:10.1109/access.2021.3060441

Cited by 36 publications

(16 citation statements)

References 28 publications

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“…(1) A single physiological signal, PPG, achieved improved Ac and Se compared with multi-sensor systems reported in [30][31][32][33][34][35][36][37][38]. Only Heo et al [29] report the use of PPG, but they achieved lower accuracy (97% for binary and 78% using a three-level mental states classification) than us. (2) We used a minimum window length of 5 s to detect three-level mental stress with very good accuracy, compared with 60 s segments reported in some works.…”

Section: Comparison With Published Researchmentioning

confidence: 82%

Real-time mental stress detection technique using neural networks towards a wearable health monitor

Mukherjee

Mukhopadhyay

Gupta

2022

Meas. Sci. Technol.

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Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

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Section: Comparison With Published Researchmentioning

confidence: 82%

Real-time mental stress detection technique using neural networks towards a wearable health monitor

Mukherjee

Mukhopadhyay

Gupta

2022

Meas. Sci. Technol.

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“…Photoplethysmography (PPG) and electrodermal activity (EDA) are the most used physiological reactions in affective computing and good indicators of physiological arousal and stress activation (Posada-Quintero & Chon, 2020; Shaffer & Ginsberg, 2017). PPG measures the blood volume pulse (BVP), and it indicates the quantity of blood flowing into the peripheral vessel, which is influenced by the automatic nervous system (Heo et al, 2021). EDA measures sweat gland activation, which is increased through the activation of the sympathetic nervous system (Posada-Quintero & Chon, 2020).…”

Section: Theoretical Mechanism: Human-environment Interactionmentioning

confidence: 99%

Wearable Sensing and Mining of the Informativeness of Older Adults’ Physiological, Behavioral, and Cognitive Responses to Detect Demanding Environmental Conditions

Torku

Chan

Yung

et al. 2022

Environment and Behavior

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Due to the decline in functional capability, older adults are more likely to encounter excessively demanding environmental conditions (that result in stress and/or mobility limitation) than the average person. Current efforts to detect such environmental conditions are inefficient and are not person-centered. This study presents a more efficient and person-centered approach that involves using wearable sensors to collect continuous bodily responses (i.e., electroencephalography, photoplethysmography, electrodermal activity, and gait) and location data from older adults to detect demanding environmental conditions. Computationally, this study developed a Random Forest algorithm—considering the informativeness of the bodily response—and a hot spot analysis-based approach to identify environmental locations with high demand. The approach was tested on data collected from 10 older adults during an outdoor environmental walk. The findings demonstrate that the proposed approach can detect demanding environmental conditions that are likely to result in stress and/or limited mobility for older adults.

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“…The HR and HRV measurements captured by the sensors not only provide information about physical health, they can also help track mental stress which has secondary deleterious effects on health, including mental health. [4][5][6][7] The most commonly used sensors for cardiovascular measurements are wrist-worn smart watches; however, chest strap sensors are also widely used, especially in the context of sports. More recently, a class of devices that are designed to be worn on the upper arm or the forearm have become commercially available.…”

Section: Introductionmentioning

confidence: 99%

“…The latter tend to be more accurate than the former. 3,4,[8][9][10][11] While there has been extensive prior work validating wrist-worn heart rate sensors, most of this work has been done in laboratory conditions. 1,4,12 Less work has been done to examine the validity of optical HR sensors in completely unconstrained and uncontrolled naturalistic settings.…”

Section: Introductionmentioning

confidence: 99%

“…3,4,[8][9][10][11] While there has been extensive prior work validating wrist-worn heart rate sensors, most of this work has been done in laboratory conditions. 1,4,12 Less work has been done to examine the validity of optical HR sensors in completely unconstrained and uncontrolled naturalistic settings. For example, a recent meta-review of 44 studies that reported on validity of wrist-worn optical sensors found only 7 studies that included daily living activities outside of a lab setting.…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of Using an Armband Optical Heart Rate Sensor in Naturalistic Environment

Kotlyar

Dufresne

et al. 2022

Preprint

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Consumer-grade heart rate (HR) sensors including chest straps, wrist-worn watches and rings have become very popular in recent years for tracking individual physiological state, training for sports and even measuring stress levels and emotional changes. While the majority of these consumer sensors are not medical devices, they can still offer insights for consumers and researchers if used correctly taking into account their limitations. Multiple previous studies have been done using a large variety of consumer sensors including Polar devices, Apple watches, and Fitbit wrist bands. The vast majority of prior studies have been done in laboratory settings where collecting data is relatively straightforward. However, using consumer sensors in naturalistic settings that present significant challenges, including noise artefacts and missing data, has not been as extensively investigated. Additionally, the majority of prior studies focused on wrist-worn optical HR sensors. Arm-worn sensors have not been extensively investigated either. In the present study, we validate HR measurements obtained with an arm-worn optical sensor (Polar OH1) against those obtained with a chest-strap electrical sensor (Polar H10) from 16 participants over a 2-week study period in naturalistic settings. We also investigated the impact of physical activity measured with 3-D accelerometers embedded in the H10 chest strap and OH1 armband sensors on the agreement between the two sensors. Overall, we find that the arm-worn optical Polar OH1 sensor provides a good estimate of HR (Pearson r = 0.90, p < 0.01). Filtering the signal that corresponds to physical activity further improves the HR estimates but only slightly (Pearson r = 0.91, p < 0.01). Based on these preliminary findings, we conclude that the arm-worn Polar OH1 sensor provides usable HR measurements in daily living conditions, with some caveats discussed in the paper.

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Stress Detection With Single PPG Sensor by Orchestrating Multiple Denoising and Peak-Detecting Methods

Cited by 36 publications

References 28 publications

Real-time mental stress detection technique using neural networks towards a wearable health monitor

Real-time mental stress detection technique using neural networks towards a wearable health monitor

Wearable Sensing and Mining of the Informativeness of Older Adults’ Physiological, Behavioral, and Cognitive Responses to Detect Demanding Environmental Conditions

Feasibility of Using an Armband Optical Heart Rate Sensor in Naturalistic Environment

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