Machine learning-based signal processing using physiological signals for stress detection

2015 ◽  
Author(s):  
Adnan Ghaderi ◽  
Javad Frounchi ◽  
Alireza Farnam
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Physiological Signals ◽  
Stress Detection

scholarly journals Stress detection using deep neural networks

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Russell Li ◽  
Zhandong Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Multilayer Perceptron ◽  
Deep Neural Networks ◽  
Physiological Signals ◽  
Emotion Classification ◽  
Stress Detection ◽  
Accuracy Rates

Abstract Background Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to physiological health and psychological wellbeing. Developing robust methods for the rapid and accurate detection of human stress is of paramount importance. Methods Prior research has shown that analyzing physiological signals is a reliable predictor of stress. Such signals are collected from sensors that are attached to the human body. Researchers have attempted to detect stress by using traditional machine learning methods to analyze physiological signals. Results, ranging between 50 and 90% accuracy, have been mixed. A limitation of traditional machine learning algorithms is the requirement for hand-crafted features. Accuracy decreases if features are misidentified. To address this deficiency, we developed two deep neural networks: a 1-dimensional (1D) convolutional neural network and a multilayer perceptron neural network. Deep neural networks do not require hand-crafted features but instead extract features from raw data through the layers of the neural networks. The deep neural networks analyzed physiological data collected from chest-worn and wrist-worn sensors to perform two tasks. We tailored each neural network to analyze data from either the chest-worn (1D convolutional neural network) or wrist-worn (multilayer perceptron neural network) sensors. The first task was binary classification for stress detection, in which the networks differentiated between stressed and non-stressed states. The second task was 3-class classification for emotion classification, in which the networks differentiated between baseline, stressed, and amused states. The networks were trained and tested on publicly available data collected in previous studies. Results The deep convolutional neural network achieved 99.80% and 99.55% accuracy rates for binary and 3-class classification, respectively. The deep multilayer perceptron neural network achieved 99.65% and 98.38% accuracy rates for binary and 3-class classification, respectively. The networks’ performance exhibited a significant improvement over past methods that analyzed physiological signals for both binary stress detection and 3-class emotion classification. Conclusions We demonstrated the potential of deep neural networks for developing robust, continuous, and noninvasive methods for stress detection and emotion classification, with the end goal of improving the quality of life.

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.

Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition

2021 ◽  
Vol 68 ◽  
pp. 102577
Author(s):  
Yang Zhou ◽  
Chaoyang Chen ◽  
Mark Cheng ◽  
Yousef Alshahrani ◽  
Sreten Franovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Methods ◽  
Semg Signal ◽  
Motion Recognition ◽  
Machine Learning Methods ◽  
Shoulder Motion

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

scholarly journals An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hayssam Dahrouj ◽  
Rawan Alghamdi ◽  
Hibatallah Alwazani ◽  
Sarah Bahanshal ◽  
Alaa Alameer Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Optimization Problems

scholarly journals Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3616
Author(s):  
Jan Ubbo van Baardewijk ◽  
Sarthak Agarwal ◽  
Alex S. Cornelissen ◽  
Marloes J. A. Joosen ◽  
Jiska Kentrop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Chemical Agent ◽  
Counter Measures ◽  
Detection And Identification ◽  
Life Saving ◽  
Future Work

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

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