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

Nath, Rajdeep Kumar; Thapliyal, Himanshu; Caban‐Holt, Allison

doi:10.1007/s11265-020-01611-5

Cited by 32 publications

(32 citation statements)

References 37 publications

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“…Unlike stress detection, anxiety detection is relatively less explored in the context of the Internet of Medical Things (IoMT). For example, there is a significant amount of research work available in the literature that uses low-cost consumer electronic components to monitor stress continuously in real time [18,19,26,27]. In [18], a smart wristband-based stress detection model was proposed.…”

Section: Related Workmentioning

confidence: 99%

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

Nath

Thapliyal

2021

SN COMPUT. SCI.

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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 are 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 wristworn 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.

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Section: Related Workmentioning

confidence: 99%

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

Nath

Thapliyal

2021

SN COMPUT. SCI.

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“…The studies [17,19,[86][87][88][89][90][91][92][93][94][95], highlighted DL-based prediction of other age-related other issues such as Type -2 diabetics, COVID-19 in older patients, coronary blockage in arteries, age-related eye diseases, brain age with old age, age-related disease gene associations, and heart stroke. Interesting findings and summary of these studies [17,19,[86][87][88][89][90][91][92][93][94][95] is provided in Tables A5 and A6 in Appendix A.…”

Section: • Prediction Over Spectrum Of Age-related Issuesmentioning

confidence: 99%

“…The use of genomic data in DL in studying age-related conditions shows promising direction, but domain knowledge is needed to guide the DL model extracting patterns [48]. Some interesting studies have shown the usage of sensor data for guiding and predicting age-related issues [87,88,95]. However, data gathered from sensors may have a certain degree of errors, noise, or redundant information due to battery or communication loss in sensor readings.…”

Section: • Prediction Over Spectrum Of Age-related Issuesmentioning

confidence: 99%

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

Wassan

Zheng

Wang

2021

Cells

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Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging).

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“…There is an increasing demand for finding solutions that support traditional assessment of the affective and cognitive states based on of the analysis of physiological signals (e.g., EDA, BVP, ECG or temperature) and employing machine or deep learning methods, which makes an independent application that predicts the patient’s condition and maximizes diagnosis accuracy [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

“…Repeated episodes of stress can disrupt a person’s physical and mental stability. Monitoring stress levels has therefore become an important and frequently addressed issue in recent years [ 40 ]. The stress level was assessed in a study using the Empatica E4 wristband and EDA signal analysis [ 46 ].…”

Section: Introductionmentioning

confidence: 99%

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Romaniszyn-Kania

Pollak

Bugdoł

et al. 2021

Sensors

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Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.

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

Cited by 32 publications

References 37 publications

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

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

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

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