scholarly journals Using Machine Learning to Train a Wearable Device for Measuring Students’ Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom Use

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4833
Author(s):  
William L. Romine ◽  
Noah L. Schroeder ◽  
Josephine Graft ◽  
Fan Yang ◽  
Reza Sadeghi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Problem Solving ◽  
Body Temperature ◽  
Cognitive Load ◽  
Real Time ◽  
Electrodermal Activity ◽  
Wearable Device ◽  
Physiological Data ◽  
Test Dataset

Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in the brain can be measured through changes in the body’s physiology, but current real-time measures tend to be unimodal and invasive. We therefore propose the concept of a wearable educational fitness (EduFit) tracker. We use machine learning with physiological data to understand how to develop a wearable device that tracks cognitive load accurately in real time. In an initial study, we found that body temperature, skin conductance, and heart rate were able to distinguish between (i) a problem solving activity (high cognitive load), (ii) a leisure activity (moderate cognitive load), and (iii) daydreaming (low cognitive load) with high accuracy in the test dataset. In a second study, we found that these physiological features can be used to predict accurately user-reported mental focus in the test dataset, even when relatively small numbers of training data were used. We explain how these findings inform the development and implementation of a wearable device for temporal tracking and logging a user’s learning activities and cognitive load.

scholarly journals Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device

2021 ◽  
Vol 18 (24) ◽  
pp. 13126
Author(s):  
Nicole E. Moyen ◽  
Rohit C. Bapat ◽  
Beverly Tan ◽  
Lindsey A. Hunt ◽  
Ollie Jay ◽  
...  
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Gold Standard ◽  
Mean Squared Error ◽  
Core Body Temperature ◽  
Absolute Error ◽  
Wearable Device ◽  
Physiological Data ◽  
Wide Range ◽  
Core Body

With climate change increasing global temperatures, more workers are exposed to hotter ambient temperatures that exacerbate risk for heat injury and illness. Continuously monitoring core body temperature (TC) can help workers avoid reaching unsafe TC. However, continuous TC measurements are currently cost-prohibitive or invasive for daily use. Here, we show that Kenzen’s wearable device can accurately predict TC compared to gold standard TC measurements (rectal probe or gastrointestinal pill). Data from four different studies (n = 52 trials; 27 unique subjects; >4000 min data) were used to develop and validate Kenzen’s machine learning TC algorithm, which uses subject’s real-time physiological data combined with baseline anthropometric data. We show Kenzen’s TC algorithm meets pre-established accuracy criteria compared to gold standard TC: mean absolute error = 0.25 °C, root mean squared error = 0.30 °C, Pearson r correlation = 0.94, standard error of the measurement = 0.18 °C, and mean bias = 0.07 °C. Overall, the Kenzen TC algorithm is accurate for a wide range of TC, environmental temperatures (13–43 °C), light to vigorous heart rate zones, and both biological sexes. To our knowledge, this is the first study demonstrating a wearable device can accurately predict TC in real-time, thus offering workers protection from heat injuries and illnesses.

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.

scholarly journals Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

2020 ◽  
Author(s):  
Sandya Subramanian ◽  
Patrick L. Purdon ◽  
Riccardo Barbieri ◽  
Emery N. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Prediction Models ◽  
Electrodermal Activity ◽  
Strong Relationship ◽  
Pulse Amplitude ◽  
Physiological Data ◽  
Statistical Tool ◽  
Autonomic Changes ◽  
Before And After

ABSTRACTDuring general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

scholarly journals Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254053
Author(s):  
Sandya Subramanian ◽  
Patrick L. Purdon ◽  
Riccardo Barbieri ◽  
Emery N. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Prediction Models ◽  
Electrodermal Activity ◽  
Strong Relationship ◽  
Pulse Amplitude ◽  
Physiological Data ◽  
Statistical Tool ◽  
Autonomic Changes ◽  
Before And After

During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

scholarly journals Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

2019 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Hugo F. Posada-Quintero ◽  
Jeffrey B. Bolkhovsky
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Memory Task ◽  
Autonomic Response ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Classification ◽  
Machine Learning Models

Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals.

scholarly journals Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3987 ◽  
Author(s):  
Toshitaka Yamakawa ◽  
Miho Miyajima ◽  
Koichi Fujiwara ◽  
Manabu Kano ◽  
Yoko Suzuki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Anomaly Detection ◽  
Real Time ◽  
False Positive Rate ◽  
Smartphone App ◽  
Heart Rate Variability Analysis ◽  
Statistical Process ◽  
Time Operation

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching

2016 ◽  
Vol 40 (5) ◽  
pp. 573-581 ◽  
Author(s):  
Ann L Edwards ◽  
Michael R Dawson ◽  
Jacqueline S Hebert ◽  
Craig Sherstan ◽  
Richard S Sutton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adaptive Control ◽  
Cognitive Load ◽  
Real Time ◽  
Case Series ◽  
Robotic Arm ◽  
User Intent ◽  
Myoelectric Prostheses ◽  
Time Prediction ◽  
Case Series Study

Background: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. Objectives: The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). Study design: Case series study. Methods: We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Results: Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Conclusion: Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. Clinical relevance Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.

scholarly journals Smart Bed Health Monitoring based on ECG and Body Temperature using Lab View and IoT

2019 ◽  
Vol 9 (2) ◽  
pp. 4631-4647
Keyword(s):  
Heart Rate ◽  
Body Temperature ◽  
Real Time ◽  
Health Monitoring ◽  
Influential Factors ◽  
Real Time Monitoring ◽  
Long Distance ◽  
Labview Software ◽  
Heart Monitoring ◽  
Monitoring Devices

Heart disease remains as the major cause of death encompassed by Malaysians over the past ten years starting from 2005 until 2014. There are many factors that influenced this statistical measurement. One of the most influential factors is due to insufficient space for medical placement provided by the hospitals. The purpose of this study is to develop a smart bed health monitoring system based on electrocardiography (ECG) and body temperature sensors. Arduino IDE software was used for Internet of Things (IoT) and LabVIEW software for real time monitoring. Data can be seen through a website, which can help the doctors to monitor their patients’ data from a long distance. The result of the project was compared with existing monitoring devices used for heart rate (Hand Wrist Heart Monitoring and Galaxy Watch) and body temperature (Rossmax Monitoring TG380 Thermometer) which available in the market. These comparisons were conducted by several experiments to analyze its accuracy and reliability. The comparison of data between real time monitoring and IoT was analyzed to check for the effectiveness of data via internet. The result showed that the highest percentage of error for both parameters of heart rate and body temperature were less than 4.2%. This system able to interpret an individual’s level of healthiness such as bradycardia, tachycardia and fever which can be monitored in real time monitoring.

Machine Learning Based IoT Geriatric Fall Intelligent System in Pandemic (Preprint)

2021 ◽  
Author(s):  
Gowri R ◽  
Rathipriya R
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Heart Rate ◽  
Real Time ◽  
Old Age ◽  
Sensor Data ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
The Real ◽  
Early Fall

UNSTRUCTURED In the current pandemic, there is lack of medical care takers and physicians in hospitals and health centers. The patients other than COVID infected are also affected by this scenario. Besides, the hospitals are also not admitting the old age peoples, and they are scared to approach hospitals even for their basic health checkups. But, they have to be cared and monitored to avoid the risk factors like fall incidence which may cause fatal injury. In such a case, this paper focuses on the cloud based IoT gadget for early fall incidence prediction. It is machine learning based fall incidence prediction system for the old age patients. The approaches such as Logistic Regression, Naive Bayes, Stochastic Gradient Descent, Decision Tree, Random Forest, Support Vector Machines, K-Nearest Neighbor and ensemble learning boosting techniques, i.e., XGBoost are used for fall incidence prediction. The proposed approach is first tested on the benchmark activity sensor data with different features for training purpose. The real-time vital signs like heart rate, blood pressure are recorded and stored in cloud and the machine learning approaches are applied to it. Then tested on the real-time sensor data like heart rate and blood pressure data of geriatric patients to predict early fall.

Circadian Rhythm Estimation by Core Body Temperature Filtered with Simultaneously Recorded Physiological Data

1997 ◽  
Vol 36 (04/05) ◽  
pp. 306-310 ◽  
Author(s):  
T. Nakano ◽  
E. Koyama ◽  
T. Imai ◽  
H. Hagiwara
Keyword(s):  
Heart Rate ◽  
Circadian Rhythm ◽  
Body Temperature ◽  
Core Body Temperature ◽  
Field Measurements ◽  
Physical Activities ◽  
Temperature Data ◽  
Physiological Data ◽  
Heart Rate Data ◽  
Core Body

Abstract.In field measurements, monitoring of core body temperature is influenced by physical activities; therefore, the estimation of circadian rhythm from the data may not be exact. The purpose of this study is to design a core body temperature filter in order to reduce artifacts induced by physical activities using simultaneously recorded physiological data such as heart rate data.The effects of physical activities on core body temperature and heart rate are assessed through three experiments. Based on the above knowledge, a core body temperature filter was designed. The filter removes part of rectal temperature data as artifact when heart rate rises above a predetermined threshold. As a result, most of the spike-like noise was removed and the filtered temperature data showed sinusoidal variation more than the unfiltered data. The mesor of the estimated rhythm significantly decreased. This filtering method can provide more precise information about circadian rhythm, especially in field measurements.

Sign in / Sign up

Export Citation Format

Share Document