Stress Detection from Multimodal Wearable Sensor Data

Abstract

Stress can be recognised by observing changes in physiological responses on the human body. Wearable sensors for stress detection are becoming more prominent in recent years due to their functionality and non-intrusive nature. By utilising data from wearable sensors, we have developed a personalized stress detection system. Our system performs classification on stress level using multimodal data from wrist-worn device Empatica E4 wearable sensor. We implemented three different classification algorithms: Logistic Regression, Decision Tree, and Random Forest and used four-class classification conditions: baseline, stress, amusement, and meditation. By evaluating the performance of the system, we demonstrate that our system can perform the best and consistent personalized stress detection using Random Forest classifier with the accuracy of 88%-99% on 15 subjects.

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

“…As a medical-grade wearable device, it enables researchers to collect multiple physiological data such as BVP for HRV analysis, and EDA that reflects the constantly fluctuating electrical properties of a certain area of skin and peripheral skin temperature. Besides, it also captures motion activity with a 3-axis accelerometer [ 80 , 81 , 82 , 83 ].…”

“… Notes: Empatica E4 wrist band is used in [ 80 , 81 , 82 , 83 ]; AutoSense is used in [ 84 , 85 , 86 ]; SleepSense is used in [ 87 , 88 ]; BN-PPGED is used in [ 89 ]; Cardiosport TP3 is used in [ 90 ]; Q-sensor is used in [ 70 ]; Wahoo chest belt is used in [ 91 ]; BioHarness 3, Shimmer sensor, and MindWave mobile EEG headset are being used as an integrated system for stress monitoring in [ 92 ]; DataLOG is used in [ 93 ]; Device 1 is a EEG wearable sensor developed in Online Predictive Tools for Intervention in Mental Illness (PTIMI) project funded by European Union [ 94 ]; Device 2 is a noninvasive physiological sensor for stress assessment presented in [ 95 ]; Device 3 is used in [ 96 ] which they collect the EMG signals of the left trapezius muscle and then remove the contained ECG signal components. …”

An Advanced Stress Detection Approach based on Processing Data from Wearable Wrist Devices

“…Applicability of the WESAD data set is shown in a few other research papers, where the authors tried to achieve improved accuracy performances by exploiting different intelligent algorithms. For example, in [8], only wrist sensor measurements from the WESAD data set are exploited, highlighting that wrist data measuring techniques are non-intrusive and widely available for acquiring. The research [8] uses three different machine learning models (i.e., logistic regression, decision tree, and www.ijacsa.thesai.org random forest) without any previous feature engineering processes.…”

“…For example, in [8], only wrist sensor measurements from the WESAD data set are exploited, highlighting that wrist data measuring techniques are non-intrusive and widely available for acquiring. The research [8] uses three different machine learning models (i.e., logistic regression, decision tree, and www.ijacsa.thesai.org random forest) without any previous feature engineering processes. The best performances were achieved with the random forest model, achieving an accuracy between 88% and 99%, depending on the exploited feature.…”

“…The research of [9] is also valuable from the perspective that it provides insights into what combination of different sensors can provide the most useful data for stress detections, highlighting the next three measuring devices: heart rate (HR) sensors, blood volume pulse (BVP), and skin temperature (ST) sensors. The most recent state-of-the-art research for automated stress detection in real life [8], [9] suggest an approach that employs a chest sensor. In their method, they first fine-tune their machine-learning model in the lab before applying it in real-world situations with certain simplifications, such as excluding times of moderate to high activity.…”