Pain and Stress Detection Using Wearable Sensors and Devices—A Review

Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.

Download Full-text

Psychological Stress Detection Using Electrodermal Activity

PsycEXTRA Dataset ◽

10.1037/e507692017-001 ◽

2017 ◽

Author(s):

A. S. Anusha ◽

S. P. Preejith ◽

Jayaraj Joseph

Keyword(s):

Psychological Stress ◽

Electrodermal Activity ◽

Stress Detection

Download Full-text

CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽

10.3390/s21010052 ◽

2020 ◽

Vol 21 (1) ◽

pp. 52

Author(s):

Tianyi Zhang ◽

Abdallah El Ali ◽

Chen Wang ◽

Alan Hanjalic ◽

Pablo Cesar

Keyword(s):

Emotion Recognition ◽

Electrodermal Activity ◽

Short Form ◽

Recognition Performance ◽

Binary Classification ◽

Wearable Sensors ◽

Recognition Algorithm ◽

Fine Grained ◽

Physiological Sensors ◽

Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

Download Full-text

Smart Wearable Sensors Based on Triboelectric Nanogenerator for Personal Healthcare Monitoring

Micromachines ◽

10.3390/mi12040352 ◽

2021 ◽

Vol 12 (4) ◽

pp. 352

Author(s):

Ruonan Li ◽

Xuelian Wei ◽

Jiahui Xu ◽

Junhuan Chen ◽

Bin Li ◽

...

Keyword(s):

Medical Care ◽

Health Monitoring ◽

Wearable Sensors ◽

The Elderly ◽

The Body ◽

Assessment System ◽

Triboelectric Nanogenerator ◽

Personal Healthcare ◽

Healthcare Monitoring ◽

Smart Wearable

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.

Download Full-text

Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Electronics ◽

10.3390/electronics10131550 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1550

Author(s):

Alexandros Liapis ◽

Evanthia Faliagka ◽

Christos P. Antonopoulos ◽

Georgios Keramidas ◽

Nikolaos Voros

Keyword(s):

Machine Learning ◽

Deep Learning ◽

User Experience ◽

Electrodermal Activity ◽

Binary Classification ◽

Research Question ◽

Classification Problem ◽

Categorical Variables ◽

Stress Detection ◽

Software Failures

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

Download Full-text

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽

10.3390/s21103461 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3461

Author(s):

Blake Anthony Hickey ◽

Taryn Chalmers ◽

Phillip Newton ◽

Chin-Teng Lin ◽

David Sibbritt ◽

...

Keyword(s):

Mental Health ◽

Systematic Review ◽

Heart Rate ◽

Heart Rate Variability ◽

Electrodermal Activity ◽

Smart Devices ◽

Stress Detection ◽

Wearable Technologies ◽

Detection Of Stress ◽

Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

Download Full-text

Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

PLoS ONE ◽

10.1371/journal.pone.0106613 ◽

2014 ◽

Vol 9 (9) ◽

pp. e106613 ◽

Cited By ~ 6

Author(s):

Roberto O. Chávez ◽

Jan G. P. W. Clevers ◽

Jan Verbesselt ◽

Paulette I. Naulin ◽

Martin Herold

Keyword(s):

Time Series ◽

Water Stress ◽

Stress Detection ◽

New Approach ◽

Ndvi Time Series

Download Full-text

Sub-second dynamics of theta-gamma coupling in hippocampal CA1

eLife ◽

10.7554/elife.44320 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 3

Author(s):

Lu Zhang ◽

John Lee ◽

Christopher Rozell ◽

Annabelle C Singer

Keyword(s):

Neural Coding ◽

Brain Activity ◽

Gamma Oscillations ◽

New Approach ◽

Excitatory State ◽

Hippocampal Ca1 ◽

Frequent Switching ◽

Brain States ◽

Oscillatory Brain Activity ◽

Oscillatory Coupling

Oscillatory brain activity reflects different internal brain states including neurons’ excitatory state and synchrony among neurons. However, characterizing these states is complicated by the fact that different oscillations are often coupled, such as gamma oscillations nested in theta in the hippocampus, and changes in coupling are thought to reflect distinct states. Here, we describe a new method to separate single oscillatory cycles into distinct states based on frequency and phase coupling. Using this method, we identified four theta-gamma coupling states in rat hippocampal CA1. These states differed in abundance across behaviors, phase synchrony with other hippocampal subregions, and neural coding properties suggesting that these states are functionally distinct. We captured cycle-to-cycle changes in oscillatory coupling states and found frequent switching between theta-gamma states showing that the hippocampus rapidly shifts between different functional states. This method provides a new approach to investigate oscillatory brain dynamics broadly.

Download Full-text