Stress Detection Using Wearable Devices based on Transfer Learning

2021 ◽  
Author(s):  
Jinting Wu ◽  
Yujia Zhang ◽  
Xiaoguang Zhao
Keyword(s):  
Transfer Learning ◽  
Wearable Devices ◽  
Stress Detection

scholarly journals Survey on Stress Detection Using Multiple Sensors through Wearable Devices

2021 ◽  
Vol 10 (2) ◽  
pp. 787-790
Keyword(s):  
Machine Learning ◽  
Wearable Devices ◽  
Anxiety And Depression ◽  
Accuracy Score ◽  
Machine Learning Method ◽  
Stress Detection ◽  
Multiple Sensors ◽  
Machine Learning Model ◽  
Wide Range ◽  
Principle Objective

An Individual method of living on with a daily existence it directly influences on your overall health. Since stress is the significant infection of our human body. Like depression, heart attack and mental illness. WHO says “Globally, more than 264 million people of all ages suffer from depression.”[8]. Also the report says that most of the time people are stressed because of their work. 10.7% of People disorder with stress, anxiety and depression [8]. There are different method to discovering stress ex. Smart watches, chest belt, and extraordinary machine. Our principle objective is to figure out pressure progressively utilizing smart watches through their Sensor. There are different kinds of sensor available to find stress such as PPG, GSR, HRV, ECG and temperature. Smart watches contain a wide range of data through various sensor. This kind of gathered information are applied on various machine learning method. Like linear regression, SVM, KNN, decision tree. Technique have distinct, comparing accuracy and chooses best Machine learning model. This paper investigation have different analysis to find and compare accuracy by various sensors data. It is also check whether using one sensor or multiple sensors such as HRV, ECG or GSR and PPG to predict the better accuracy score for stress detection.

scholarly journals Continuous Stress Detection of Hospital Staff Using Smartwatch Sensors and Classifier Ensemble

2021 ◽  
Author(s):  
Muhammad Ali Fauzi ◽  
Bian Yang
Keyword(s):  
Stress Level ◽  
High Stress ◽  
Hospital Staff ◽  
Wearable Devices ◽  
Classifier Ensembles ◽  
Stress Detection ◽  
Monitoring Task ◽  
The Ensemble Method ◽  
The Individual ◽  
Voting Strategy

High stress levels among hospital workers could be harmful to both workers and the institution. Enabling the workers to monitor their stress level has many advantages. Knowing their own stress level can help them to stay aware and feel more in control of their response to situations and know when it is time to relax or take some actions to treat it properly. This monitoring task can be enabled by using wearable devices to measure physiological responses related to stress. In this work, we propose a smartwatch sensors based continuous stress detection method using some individual classifiers and classifier ensembles. The experiment results show that all of the classifiers work quite well to detect stress with an accuracy of more than 70%. The results also show that the ensemble method obtained higher accuracy and F1-measure compared to all of the individual classifiers. The best accuracy was obtained by the ensemble with soft voting strategy (ES) with 87.10% while the hard voting strategy (EH) achieved the best F1-measure with 77.45%.

scholarly journals Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4271 ◽  
Author(s):  
Frédéric Li ◽  
Kimiaki Shirahama ◽  
Muhammad Adeel Nisar ◽  
Xinyu Huang ◽  
Marcin Grzegorzek
Keyword(s):  
Time Series ◽  
Ubiquitous Computing ◽  
Transfer Learning ◽  
Time Series Data ◽  
Wearable Devices ◽  
Series Data ◽  
Target Field ◽  
Proper Training ◽  
Application Fields ◽  
General Architecture

The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems—Human Activity Recognition (HAR) and Emotion Recognition (ER)—and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.

scholarly journals Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices

2021 ◽  
Author(s):  
Van-Tu Ninh ◽  
Sinéad Smyth ◽  
Minh-Triet Tran ◽  
Cathal Gurrin
Keyword(s):  
High Resolution ◽  
Stress Level ◽  
Electrodermal Activity ◽  
Low Cost ◽  
Wearable Devices ◽  
Detection Accuracy ◽  
Stress Detection ◽  
Low Resolution ◽  
Detection Model ◽  
Training Stress

Identifying stress level can provide valuable data for mental health analytics as well as labels for annotation systems. Although much research has been conducted into stress detection models using heart rate variability at a higher cost of data collection, there is a lack of research on the potential of using low-resolution Electrodermal Activity (EDA) signals from consumer-grade wearable devices to identify stress patterns. In this paper, we concentrate on performing statistical analyses on the stress detection capability of two popular approaches of training stress detection models with stress-related biometric signals: user-dependent and user-independent models. Our research manages to show that user-dependent models are statistically more accurate for stress detection. In terms of effectiveness assessment, the balanced accuracy (BA) metric is employed to evaluate the capability of distinguishing stress and non-stress conditions of the models trained on either low-resolution or high-resolution Electrodermal Activity (EDA) signals. The results from the experiment show that training the model with (comparatively low-cost) low-resolution EDA signal does not affect the stress detection accuracy of the model significantly compared to using a high-resolution EDA signal. Our research results demonstrate the potential of attaching the user-dependent stress detection model trained on personal low-resolution EDA signal recorded to collect data in daily life to provide users with personal stress level insight and analysis.

scholarly journals Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1849 ◽  
Author(s):  
Yekta Said Can ◽  
Niaz Chalabianloo ◽  
Deniz Ekiz ◽  
Cem Ersoy
Keyword(s):  
Detection System ◽  
Wearable Sensors ◽  
Real Life ◽  
Extraction Methods ◽  
Wearable Devices ◽  
Physiological Data ◽  
Stress Detection ◽  
Negative Effects ◽  
Extreme Stress ◽  
High Level

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

scholarly journals HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2873
Author(s):  
Kayisan M. Dalmeida ◽  
Giovanni L. Masala
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Learning Model ◽  
Wearable Devices ◽  
Mental Wellbeing ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stress Detection ◽  
Machine Learning Model ◽  
Stress Levels

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence, the study will be mainly focusing on heart rate variability (HRV). This study is aimed at investigating the role of HRV-derived features as stress markers. This is achieved by developing a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, by testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from heart rate measurements obtained from wearable devices. We demonstrate that HRV features constitute good markers for stress detection as the best machine learning model developed achieved a Recall of 80%. Furthermore, this study indicates that HRV metrics such as the Average of normal-to-normal (NN) intervals (AVNN), Standard deviation of the average NN intervals (SDNN) and the Root mean square differences of successive NN intervals (RMSSD) were important features for stress detection. The proposed method can be also used on all applications in which is important to monitor the stress levels in a non-invasive manner, e.g., in physical rehabilitation, anxiety relief or mental wellbeing.

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