The association between day-to-day stress experiences and work-life interference among office workers in academia: an ecological momentary assessment study.

PURPOSE: We investigated relations between day-to-day job demands, job control, job strain, social support at work, and day-to-day work-life interference among office workers in academia. METHODS: This study is based on a 15-working day data collection period using an Ecological Momentary Assessment (EMA) implemented in our self-developed STRAW smartphone application. We recruited office workers from two academic settings in Belgium and Slovenia. Participants were repeatedly asked to complete EMAs including work stressors and work interfering with personal life (WIPL) as well as personal life interfering with work (PLIW). We applied fixed-effect model testing with random intercepts to investigate within- and between-participant levels. RESULTS: We included 55 participants with 2261 analyzed observations in this study. Our data showed that researchers with a PhD reported higher WIPL compared to administrative and technical staff (ß = 0.37, p < 0.05). We found significant positive associations between job demands (ß = 0.53, p < 0.001), job control (ß = 0.19, p < 0.01), and job strain (ß = 0.61, p < 0.001) and WIPL. Furthermore, there was a significant interaction effect between job control and social support at work on WIPL (ß = - 0.24, p < 0.05). Additionally, a significant negative association was found between job control and PLIW (ß = - 0.20, p < 0.05). CONCLUSION: Based on our EMA study, higher job demands and job strain were correlated with higher WIPL. Furthermore, we found associations going in opposite directions; higher job control was correlated with higher WIPL and lower PLIW. Higher job control leading to higher imbalance stands out as a novel result.

HARE: Unifying the Human Activity Recognition Engineering Workflow.

Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE's multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.

Work environment risk factors causing day-to-day stress in occupational settings: a systematic review.

BACKGROUND: While chronic workplace stress is known to be associated with health-related outcomes like mental and cardiovascular diseases, research about day-to-day occupational stress is limited. This systematic review includes studies assessing stress exposures as work environment risk factors and stress outcomes, measured via self-perceived questionnaires and physiological stress detection. These measures needed to be assessed repeatedly or continuously via Ecological Momentary Assessment (EMA) or similar methods carried out in real-world work environments, to be included in this review. The objective was to identify work environment risk factors causing day-to-day stress. METHODS: The search strategies were applied in seven databases resulting in 11833 records after deduplication, of which 41 studies were included in a qualitative synthesis. Associations were evaluated by correlational analyses. RESULTS: The most commonly measured work environment risk factor was work intensity, while stress was most often framed as an affective response. Measures from these two dimensions were also most frequently correlated with each other and most of their correlation coefficients were statistically significant, making work intensity a major risk factor for day-to-day workplace stress. CONCLUSIONS: This review reveals a diversity in methodological approaches in data collection and data analysis. More studies combining self-perceived stress exposures and outcomes with physiological measures are warranted.

What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges.

From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was using sensor data from one location to recognize activities with sensors in another location, while in the following year, the main challenge was using the sensor data of one person to recognize the activities of other persons. We use these two challenge scenarios as a framework in which to analyze the effectiveness of different components of a machine-learning pipeline for activity recognition. We show that: (i) selecting an appropriate (location-specific) portion of the available data for training can improve the F1 score by up to 10 percentage points (p. p.) compared to a more naive approach, (ii) separate models for human locomotion and for transportation in vehicles can yield an increase of roughly 1 p. p., (iii) using semi-supervised learning can, again, yield an increase of roughly 1 p. p., and (iv) temporal smoothing of predictions with Hidden Markov models, when applicable, can bring an improvement of almost 10 p. p. Our experiments also indicate that the usefulness of advanced feature selection techniques and clustering to create person-specific models is inconclusive and should be explored separately in each use-case.

A General Framework for Making Context-Recognition Systems More Energy Efficient.

Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions-in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.

Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels.

Understanding people's eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical and real-time eating monitoring in an unobtrusive, accessible, and affordable way. To this end, we present a novel approach for the detection of eating segments with a wrist-worn device and fusion of deep and classical machine learning. It integrates a novel data selection method to create the training dataset, and a method that incorporates knowledge from raw and virtual sensor modalities for training with highly imbalanced datasets. The proposed method was evaluated using data from 12 subjects recorded in the wild, without any restriction about the type of meals that could be consumed, the cutlery used for the meal, or the location where the meal took place. The recordings consist of data from accelerometer and gyroscope sensors. The experiments show that our method for detection of eating segments achieves precision of 0.85, recall of 0.81, and F1-score of 0.82 in a person-independent manner. The results obtained in this study indicate that reliable eating detection using in the wild recorded data is possible with the use of wearable sensors on the wrist.

Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach.

Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network.

Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.

A Personal Decision Support System for Heart Failure Management (HeartMan): study protocol of the HeartMan randomized controlled trial.

BACKGROUND: Heart failure (HF) is a highly prevalent chronic disease, for which there is no cure available. Therefore, improving disease management is crucial, with mobile health (mHealth) being a promising technology. The aim of the HeartMan study is to evaluate the effect of a personal mHealth system on top of standard care on disease management and health-related quality of life (HRQoL) in HF. METHODS: HeartMan is a randomized controlled 1:2 (control:intervention) proof-of-concept trial, which will enrol 120 stable ambulatory HF patients with reduced ejection fraction across two European countries. Participants in the intervention group are equipped with a multi-monitoring health platform with the HeartMan wristband sensor as the main component. HeartMan provides guidance through a decision support system on four domains of disease management (exercise, nutrition, medication adherence and mental support), adapted to the patient's medical and psychological profile. The primary endpoint of the study is improvement in self-care and HRQoL after a six-months intervention. Secondary endpoints are the effects of HeartMan on: behavioural outcomes, illness perception, clinical outcomes and mental state. DISCUSSION: HeartMan is technologically the most innovative HF self-management support system to date. This trial will provide evidence whether modern mHealth technology, when used to its full extent, can improve HRQoL in HF. TRIAL REGISTRATION: This trial has been registered on https://clinicaltrials.gov/ct2/show/NCT03497871 , on April 13 2018 with registration number NCT03497871.

Monitoring stress with a wrist device using context.

Being able to detect stress as it occurs can greatly contribute to dealing with its negative health and economic consequences. However, detecting stress in real life with an unobtrusive wrist device is a challenging task. The objective of this study is to develop a method for stress detection that can accurately, continuously and unobtrusively monitor psychological stress in real life. First, we explore the problem of stress detection using machine learning and signal processing techniques in laboratory conditions, and then we apply the extracted laboratory knowledge to real-life data. We propose a novel context-based stress-detection method. The method consists of three machine-learning components: a laboratory stress detector that is trained on laboratory data and detects short-term stress every 2min; an activity recognizer that continuously recognizes the user's activity and thus provides context information; and a context-based stress detector that uses the outputs of the laboratory stress detector, activity recognizer and other contexts, in order to provide the final decision on 20-min intervals. Experiments on 55days of real-life data showed that the method detects (recalls) 70% of the stress events with a precision of 95%.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin.

BACKGROUND: Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg). RESULTS: We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine. CONCLUSIONS: Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.

How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject's daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data).

Activity Recognition for Diabetic Patients Using a Smartphone.

Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines machine-learning and symbolic reasoning to recognise high-level lifestyle activities using sensor data obtained primarily from the patient's smartphone. We compare five methods for machine-learning which differ in the amount of manually labelled data by the user, to investigate the trade-off between the labelling effort and recognition accuracy. In an evaluation on real-life data, the highest accuracy of 83.4 % was achieved by the MCAT method, which is capable of gradually adapting to each user.

Integrating artificial and human intelligence into tablet production process.

We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.

Protocol of the study for predicting empathy during VR sessions using sensor data and machine learning.

Virtual reality (VR) technology is often referred to as the 'ultimate empathy machine' due to its capability to immerse users in alternate perspectives and environments beyond their immediate physical reality. In this study, participants will be immersed in 3-dimensional 360° VR videos where actors express different emotions (sadness, happiness, anger, and anxiousness). The primary objective is to investigate the potential relationship between participants' empathy levels and the changes in their physiological attributes. The empathy levels will be self-reported with questionnaires, and physiological attributes will be measured using different sensors. The main outcome of the study will be a machine learning model to predict a person's empathy level based on their physiological responses while watching VR videos. Despite the existence of established methodologies and metrics in research and clinical domains, our aim is to contribute to addressing the gap of a universally accepted "gold standard" for assessing empathy. Additionally, we expect to deepen our understanding of the relationship between different emotions and psychological attributes, gender differences in empathy, and the impact of narrative context on empathic responses.

Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation.

Single-site multi-wavelength (MW) pulse transit time (PTT) measurement was recently proposed using contact sensors with sequential illumination. It leverages different penetration depths of light to measure the traversal of a cardiac pulse between skin layers. This enabled continuous single-site MW blood pressure (BP) monitoring, but faces challenges like subtle skin compression, which importantly influences the PPG morphology and subsequent PTT. We extended this idea to contact-free camera-based sensing and identified the major challenge of color channel overlap, which causes the signals obtained from a consumer RGB camera to be a mixture of responses in different wavelengths, thus not allowing for meaningful PTT measurement. To address this, we propose novel camera-independent data-driven channel separation algorithms based on constrained genetic algorithms. We systematically validated the algorithms on camera recordings of palms and corresponding ground-truth BP measurements of 13 subjects in two different scenarios, rest and activity. We compared the proposed algorithms against established blind source separation methods and against previous camera-specific physics-based method, showing good performance in both PTT reconstruction and BP estimation using a Random Forest regressor. The best-performing algorithm achieved mean absolute errors (MAEs) of 3.48 and 2.61 mmHg for systolic and diastolic BP in a leave-one-subject-out experiment with personalization, solidifying the proposed algorithms as enablers of novel contact-free MW PTT and BP estimation.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Adult height prediction using the growth curve comparison method.

Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth.

Overcoming Data Scarcity in Human Activity Recognition.

Wearable sensors have become increasingly popular in recent years, with technological advances leading to cheaper, more widely available, and smaller devices. As a result, there has been a growing interest in applying machine learning techniques for Human Activity Recognition (HAR) in healthcare. These techniques can improve patient care and treatment by accurately detecting and analyzing various activities and behaviors. However, current approaches often require large amounts of labeled data, which can be difficult and time-consuming to obtain. In this study, we propose a new approach that uses synthetic sensor data generated by 3D engines and Generative Adversarial Networks to overcome this obstacle. We evaluate the synthetic data using several methods and compare them to real-world data, including classification results with baseline models. Our results show that synthetic data can improve the performance of deep neural networks, achieving a better F1-score for less complex activities on a known dataset by 8.4% to 73% than state-of-the-art results. However, as we showed in a self-recorded nursing activity dataset of longer duration, this effect diminishes with more complex activities. This research highlights the potential of synthetic sensor data generated from multiple sources to overcome data scarcity in HAR.