Automatic motion artifact detection in electrodermal activity data using machine learning

2022 ◽  
Vol 74 ◽  
pp. 103483
Author(s):  
Md-Billal Hossain ◽  
Hugo F. Posada-Quintero ◽  
Youngsun Kong ◽  
Riley McNaboe ◽  
Ki H. Chon
Keyword(s):  
Machine Learning ◽  
Electrodermal Activity ◽  
Motion Artifact ◽  
Activity Data ◽  
Artifact 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.

scholarly journals Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohammad Nahid Hossain ◽  
Mohammad Helal Uddin ◽  
K. Thapa ◽  
Md Abdullah Al Zubaer ◽  
Md Shafiqul Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Learning Algorithm ◽  
Negative Impact ◽  
Gradient Boosting ◽  
Feature Selection Technique ◽  
Activity Data ◽  
Physical Data ◽  
Machine Learning Model

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person’s cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals’ neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method “Pearson’s correlation” and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

scholarly journals Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data (Preprint)

2019 ◽  
Author(s):  
Donna L Coffman ◽  
Xizhen Cai ◽  
Runze Li ◽  
Noelle R Leonard
Keyword(s):  
Life Events ◽  
Adolescent Mothers ◽  
Electrodermal Activity ◽  
Real Life ◽  
Motion Artifacts ◽  
Ambulatory Setting ◽  
Activity Data ◽  
Dry Electrode ◽  
Challenges And Opportunities ◽  
Initial Processing

BACKGROUND Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. OBJECTIVE This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. METHODS We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. RESULTS We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. CONCLUSIONS The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

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