A usability study of physiological measurement in school using wearable sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps

Active learning for electrodermal activity classification

To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier. However, training such a model can require much effort on the part of the researcher; this often takes the form of manually labeling portions of signal needed to represent the concept being trained. Active learning is a technique for reducing human effort by developing a classifier that can intelligently select the most relevant data samples and ask for labels for only those samples, in an iterative process. In this paper we demonstrate that active learning can reduce the labeling effort required of researchers by as much as 84% for our application, while offering equivalent or even slightly improved machine learning performance.MIT Media Lab ConsortiumRobert Wood Johnson Foundatio

An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting

Objective. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance and could be used in clinical settings in which patients cannot self-report pain, such as during surgery or when in a coma. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings while salvaging as much useful information as possible. Approach. In this study, we collected EDA data from 70 subjects while they were undergoing surgery in the operating room. We then built a fully automated artifact removal framework to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data. This automated framework consisted of first utilizing three unsupervised machine learning methods for anomaly detection, and then customizing the threshold to separate artifact for each data instance by taking advantage of the statistical properties of the artifact in that data instance. We also created simulated surgical data by introducing artifacts into cleaned surgical data and measured the performance of all three methods in removing it. Main results. Our method achieved the highest overall accuracy and precision and lowest overall error on simulated data. One of the other methods prioritized high sensitivity while sacrificing specificity and precision, while the other had low sensitivity, high error, and left behind several artifacts. These results were qualitatively similar between the simulated data instances and operating room data instances. Significance. Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery, which is the first step to enable clinical integration of EDA as part of standard monitoring

Bilateral comparison of traditional and alternate electrodermal measurement sites

Abstract Advances in mobile and wireless technology have expanded the scope of electrodermal research. Since traditional electrodermal measurement sites are not always suitable for laboratory research and are rarely appropriate for ambulatory measurements, there is a need to explore and contrast alternate measurement locations. We evaluated bilateral electrodermal activity (EDA) from five measurement sites (fingers, feet, wrists, shoulders, and calves). In a counterbalanced, randomized, within-subjects design study, participants (N = 115) engaged in a 4-min-long breathing exercise and were exposed to emotionally laden and neutral stimuli. High within-subject correlations were found between the EDA measured from fingers bilaterally (r = .89), between the left fingers and both feet (r = .72). Moderate correlations were found between EDA measured from the left fingers and wrists (r = .30 and r = .33), low correlations between the left fingers and the shoulders (r = ?.03 and r = ?.06) or calves (r = .05 and r = .14). Response latency was the shortest on the fingers while it was the longest on the lower body. Short response windows would miss some of the responses from the palmar surfaces and a substantial number from other evaluated locations. The fingers and the feet are the most reliable locations to measure from, followed by the wrists. We suggest setting site-specific response windows for different measurement locations. An investigation of repeatability showed that within-subject correlations, response frequencies, response amplitudes show a similar pattern from the first measurement time to a later one

Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness

Addressing Data Quality Challenges in Observational Ambulatory Studies: Analysis, Methodologies and Practical Solutions for Wrist-worn Wearable Monitoring

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable sensing technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient follow-up by shifting from subjective, intermittent self-reporting to objective, continuous monitoring. However, collecting and analyzing wearable data presents unique challenges, such as data entry errors, non-wear periods, missing wearable data, and wearable artifacts. We therefore present an in-depth exploration of data analysis challenges tied to wrist-worn wearables and ambulatory label acquisition, using two real-world datasets (i.e., mBrain21 and ETRI lifelog2020). We introduce novel practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized wearable non-wear detection pipeline. Further, we propose a visual analytics approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we investigate the impact of missing wearable data on "window-of-interest" analysis methodologies. Prioritizing transparency and reproducibility, we offer open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for wearable data collection and analysis in chronic disease management.Comment: 29 pages, 16 figure

Psychophysiology in the digital age

The research I performed for my thesis revolved around the question how affect-physiology dynamics can be best measured in daily life. In my thesis I focused on three aspects of this question: 1) Do wearable wristband devices have sufficient validity to capture ANS activity? 2) To what extent is the laboratory design suitable to measure affect-ANS dynamics? 3) Are the affect-ANS dynamics subject to individual differences, both in the laboratory and in daily life? In chapter 2, I validated a shortened version of the Sing-a-Song Stress (SSST) test, the SSSTshort. The purpose of this test is to create social-evaluative stress in participants through a simple and brief design that does not require the involvement of multiple confederates. The results indicated that the SSSTshort was effective in inducing ANS and affective reactivity. This makes the SSSTshort a cost-effective alternative to the well-known Trier-Social-Stress task (TSST), which can be easily incorporated into large-scale studies to expand the range of stress types that can be studied in laboratory designs. In chapter 3, I validated a new wrist worn technology for measuring electrodermal activity (EDA). As expected, the overall EDA levels measured on the wrist were lower than those measured on the palm, likely due to the lower density of sweat glands on the wrist. The analysis demonstrated that the frequency measure of non-specific skin conductance response (ns.SCR) was superior to the commonly used measure of skin conductance level (SCL) for both the palm and wrist. The wrist-based ns.SCR measure was sensitive to the experimental manipulations and showed similar correspondence to the pre-ejection period (PEP) as palm-based ns.SCR. Moreover, wrist-based ns.SCR demonstrated similar predictive validity for affective state as PEP. However, the predictive validity of both wrist-based ns.SCR and PEP was lower compared to palm-based ns.SCR. These findings suggest that wrist-based ns.SCR EDA parameter has a promising future for use in psychophysiological research. In Chapter 4 of my thesis, I conducted the first study to directly compare the relationship between affect and ANS activity in a laboratory setting to that in daily life. To elicit stress in the laboratory, four different stress paradigms were employed, while stressful events in daily life were left to chance. In both settings, a valence and arousal scale was constructed from a nine-item affect questionnaire, and ANS activity was collected using the same devices. Data was collected from a single population, and the affect-ANS dynamics were analyzed using the same methodology for both laboratory and daily life settings. The results showed a remarkable similarity between the laboratory and daily life affect-ANS relationships. In Chapter 5 of my thesis, I investigated the influence of individual differences in physical activity and aerobic fitness on ANS and affective stress reactivity. Previous research has yielded inconsistent results due to heterogeneity issues in the population studied, stressor type, and the way fitness was measured. My study made a unique contribution to this field by measuring physical activity in three ways: 1) as objective aerobic fitness, 2) leisure time exercise behavior, and 3) total moderate-to-vigorous exercise (including both exercise and all other regular physical activity behaviors). In addition, we measured the physiological and affective stress response in both a laboratory and daily life setting. The total amount of physical activity showed more relationships with stress reactivity compared to exercise behavior alone, suggesting that future research should include a total physical activity variable. Our results did not support the cross-stressor adaptation hypotheses, suggesting that if exercise has a stress-reducing effect, it is unlikely to be mediated by altered ANS regulation due to repeated exposure to physical stress

Handling Missing Data For Sleep Monitoring Systems

Sensor-based sleep monitoring systems can be used to track sleep behavior on a daily basis and provide feedback to their users to promote health and well-being. Such systems can provide data visualizations to enable self-reflection on sleep habits or a sleep coaching service to improve sleep quality. To provide useful feedback, sleep monitoring systems must be able to recognize whether an individual is sleeping or awake. Existing approaches to infer sleep-wake phases, however, typically assume continuous streams of data to be available at inference time. In real-world settings, though, data streams or data samples may be missing, causing severe performance degradation of models trained on complete data streams. In this paper, we investigate the impact of missing data to recognize sleep and wake, and use regression-and interpolation-based imputation strategies to mitigate the errors that might be caused by incomplete data. To evaluate our approach, we use a data set that includes physiological traces-collected using wristbands-, behavioral data-gathered using smartphones-and self-reports from 16 participants over 30 days. Our results show that the presence of missing sensor data degrades the balanced accuracy of the classifier on average by 10-35 percentage points for detecting sleep and wake depending on the missing data rate. The impu-tation strategies explored in this work increase the performance of the classifier by 4-30 percentage points. These results open up new opportunities to improve the robustness of sleep monitoring systems against missing data

Wireless sensors system for stress detection by means of ECG and EDA acquisition

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application