Stress Detection by Machine Learning and Wearable Sensors

2021 ◽  
Author(s):  
Prerna Garg ◽  
Jayasankar Santhosh ◽  
Andreas Dengel ◽  
Shoya Ishimaru
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Stress Detection

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

Electronics ◽  
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.

ApneaDetector

2021 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Xianda Chen ◽  
Yifei Xiao ◽  
Yeming Tang ◽  
Julio Fernandez-Mendoza ◽  
Guohong Cao
Keyword(s):  
Machine Learning ◽  
Sleep Apnea ◽  
Wearable Sensors ◽  
Machine Learning Techniques ◽  
Clinical Test ◽  
Learning Techniques ◽  
Apnea Detection ◽  
Hospital Visits ◽  
Calibration Techniques ◽  
Data Calibration

Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

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.

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