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

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

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

Vision and Radar Steering Reduces Agricultural Sprayer Operator Stress without Compromising Steering Performance

Self-propelled agricultural sprayer operators work an average of 15 h d-1 in peak season, and steering is the task that causes the operator the most stress because of the large number of stimuli involved. Automatic guidance systems help reduce stress and fatigue for operators by allowing them to focus on tasks other than steering. Physiological signals like skin conductance (electrodermal activity, EDA) change with stress and can be used to identify stressful events. The objective of this study was to determine if using a commercially available vision and radar guidance system (VSN®, Raven Industries) reduces agricultural sprayer operators’ stress compared to when they are steering manually. Four male professional sprayer operators participated in this study. Each operator performed his job duties normally in GPS-guidance-planted fields, at his self-selected speed, except to drive some passes manually and others with VSN in the same field. EDA was measured with an Empatica E4 wristband, and stressful events were quantified. Machine data (e.g., speed, RTK-GPS, and VSN metrics) were collected from each sprayer via CAN logs. The steering type, stress rate (e.g., stressful events min-1), and steering performance (crosstrack error standard deviation, XTE SD) were determined for each pass. In total, 51 passes (23 manual, 28 VSN) in six fields were analyzed. Operators using VSN had a significant reduction (48% lower, p \u3c 0.001) in their stress rate compared to when they were steering manually. There was no significant difference in the XTE SD for the steering type. The use of an automatic guidance system such as VSN could have a dramatic positive effect on the health of sprayer operators, especially during the long workdays of the peak spraying season, and could reduce the negative effects that stress and fatigue have on steering performance, mistakes, and accidents

An Observational Study With the Janssen Autism Knowledge Engine (JAKE((R))) in Individuals With Autism Spectrum Disorder

Objective: The Janssen Autism Knowledge Engine (JAKE(R)) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components. Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures. Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was easy (69%, 74/108) or very easy (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93-100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported. Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991

Autonoumous Nervous System biosignal processing via EDA and HRV from a wearable device

The assessment of changes in the autonomous nervous system (ANS) with certain diseases and pathologies conditions, has been demonstrated to have important prognostic and diagnostic value, so delineating the role of autonomous activity is important to prevent health diseases. There are many approaches to directly measure the sympathetic and parasympathetic nervous system, although, most of themare invasive and unable to provide continuous monitoring, leading to inaccurate assessment of the autonomous nervous system. Heart rate variability (HRV) and Electrodermal activity (EDA) are presented has noninvasive methods to assess the ANS, by computing the spectral analysis of both HRV and EDA biosignals. The combination of these signals is necessary to correctly measure the activity of the sympathetic and parasympathetic system, due to the fact that frequency analysis of HRV only provides the level of unbalance between these two systems, while EDA reflects only activity from the sympathetic system. ANS biosignal processing via HRV and EDA from a wearable device was studied in this thesis, in order to provide continuous monitoring. A wearable device is the ideal solution, as HRV can be calculated with photoplethysmography signals from the wrist and EDA from the fingers, providing wireless and continuous monitoring of the subjects. The extraction of the HRV and EDA features, that describe the activity of the sympathetic and parasympathetic system, were obtained by submitting the subjects to a mental arithmetic stress test, and then compared to the baseline values, in order to verify changes in the autonomous nervous system between the two situations. The distinct response to stress for the subjects was then predicted usingmachine-learning classification mechanisms, with the ability to predict how the subject will respond when submitted to a situation of stress, using only time-domain features, instead of frequency-domain features, which reduces the time needed to performthe classification

An Observational Study With the Janssen Autism Knowledge Engine (JAKE®) in Individuals With Autism Spectrum Disorder

Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components.Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures.Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was “easy” (69%, 74/108) or “very easy” (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93–100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported.Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies.Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT0266899

Burnout symptoms in forensic mental health nurses:Results from a longitudinal study

Burnout in nursing staff is a major cause for turnover and absenteeism. Identifying risk and protective factors may be helpful in decreasing burnout symptoms. Moreover, research indicates that ambulatory assessments of the autonomic nervous system might be helpful in detecting long‐term stress and burnout symptoms. One hundred and ten forensic nursing staff members completed questionnaires measuring experiences with aggressive behaviour, emotional intelligence, personality, and job stress during four waves of data collection across a 2‐year period. Multilevel analyses were used to test the predicted associations and moderation effects with (the development of) burnout symptoms. Burnout was predicted by a combination of emotional intelligence, job stress, aggression, personality factors, and skin conductance, but no moderation effects over time were found. Over a period of 2 years, the model approximately predicts a change in one burnout category on the Maslach Burnout Inventory. The amount of burnout symptoms in nurses might be used as an indicator to predict turnover and absenteeism considering the increase in symptoms over time. Nursing staff who experience severe aggression and who have relatively low levels of emotional intelligence and altruism and high levels of neuroticism and job stress should be monitored and supported to decrease the risk of burnout. Staff members can be trained to increase their emotional intelligence and relieve stress to decrease their burnout symptoms and turnover and absenteeism on the long term. Ambulatory assessment might be helpful as a nonintrusive way to detect increasing levels of burnout