Continuous Physiological Data Research Articles

The Use of Pattern Recognition to Augment Traditional Monitoring in the Prevention of Opioid Overdose Harm.

The aim of the study was to investigate the correlation of a pattern recognition algorithm to the opioid overdose intervention activities of trained medical staff at a safe consumption site (SCS). Continuous physiological data were collected using the Masimo Radius PPG pulse oximeter from volunteer users of nonprescribed, unregulated opioids at a SCS. The algorithm retrospectively calculated opioid-induced respiratory depression (OIRD) severity scores (Opioid Halo scores) were compared to interventions recorded by SCS staff. The study included data prospectively collected from 167 individuals, who underwent 370 sessions of intravenous injection of nonprescribed, unregulated opioids (Fentanyl). Interventions were documented for 150 sessions (~41%) by the SCS staff. The remaining 220 sessions had no interventions documented. The algorithm demonstrated a strong correlation with the intervention activities (Spearman ρ = 0.80, P < 0.001). The area under the receiver operating curve for the correlation with intervention activities (ie, supplemental oxygen or naloxone administration) was 0.94. The OIRD severity scores were significantly higher (P < 0.001) in sessions requiring interventions compared to nonintervention sessions. In this study, the algorithm generated OIRD severity scores had a strong correlation with the intervention activities provided by SCS staff who were blinded to the study pulse oximeter and algorithm scores. This suggests that the algorithm may be useful in detecting severe opioid-induced respiratory depression for which intervention is needed.

Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data

BackgroundPostoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs.MethodsA prospective study was conducted at the Department of Cardiovascular Surgery of West China Hospital, Sichuan University, from August 2021 to December 2022. We examined 100 cardiac valvular surgery patients, where wearable technology was utilized to collect and analyze nocturnal physiological data at the 24-hour admission, in conjunction with clinical data extraction from the Hospital Information System’s electronic records. We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. Stratified 5-fold cross-validation was used to evaluate the generalization performance. The significance of AUC differences between models was tested using the DeLong test to determine the optimal prognostic model comprehensively. Additionally, univariate logistic regression analysis was conducted on the features of the best-performing model to understand the impact of individual feature on PPCs.ResultsIn this study, 22 patients (22%) developed PPCs. Across classifiers, models combining both physiological and clinical features performed better than physiological or clinical features alone. Specifically, including physiological data in the classification model improved AUC, ACC, F1, and precision by an average of 8.32%, 1.80%, 3.28% and 6.06% compared to using clinical data only. The XGB classifier, utilizing both dataset, achieved the highest performance with an AUC of 0.82 (± 0.08) and identified eight significant features. The DeLong test indicated that the XGB model utilizing the both dataset significantly outperformed the XGB models trained on the physiological or clinical datasets alone. Univariate logistic regression analysis suggested that surgical methods, age, nni_50, and min_ven_in_mean are significantly associated with the occurrence of PPCs.ConclusionThe integration of continuous wearable physiological and clinical data significantly improves preoperative risk assessment for PPCs, which helps to optimize surgical management and reduce PPCs morbidity and mortality.

Система контроля концентрации антигенов в организме человека.

The detection of antigens plays a key role in many diagnostic tests, but current methods often prove to be complex and expensive. There is a pressing need for a simple, fast, reliable, and cost-effective system for quantitative determination of antigens, suitable for both users and laboratories (photometer). The system will analyze patient fluid samples for a group of viruses, using monoclonal antibodies and different wavelengths of light measured by the photometer. Today, many people actively monitor their health and purchase expensive "smartwatches" to keep track of key indicators. The research hypothesis is that by analyzing continuous physiological data collected through a wearable device and information provided by patients via a cloud analytical platform, it is possible to detect physiological changes and important clinical signals that may indicate the early stages of various diseases, including viral infections. The sensor can play a special role during pandemics. Bluetooth connectivity with a smartphone and «smartwatches» equipped with the appropriate application will allow real-time remote transmission of all physiological data. Socially, such an analytical system will help people monitor critical health indicators, ensure early disease detection, and study multiple antigens simultaneously. This will give individuals confidence in their health status and the ability to improve their quality of life.

Exploring the Dynamics of Canine-Assisted Interactions: A Wearable Approach to Understanding Interspecies Well-Being.

Canine-assisted interactions (CAIs) have been explored to offer therapeutic benefits to human participants in various contexts, from addressing cancer-related fatigue to treating post-traumatic stress disorder. Despite their widespread adoption, there are still unresolved questions regarding the outcomes for both humans and animals involved in these interactions. Previous attempts to address these questions have suffered from core methodological weaknesses, especially due to absence of tools for an efficient objective evaluation and lack of focus on the canine perspective. In this article, we present a first-of-its-kind system and study to collect simultaneous and continuous physiological data from both of the CAI interactants. Motivated by our extensive field reviews and stakeholder feedback, this comprehensive wearable system is composed of custom-designed and commercially available sensor devices. We performed a repeated-measures pilot study, to combine data collected via this system with a novel dyadic behavioral coding method and short- and long-term surveys. We evaluated these multimodal data streams independently, and we further correlated the psychological, physiological, and behavioral metrics to better elucidate the outcomes and dynamics of CAIs. Confirming previous field results, human electrodermal activity is the measure most strongly distinguished between the dyads' non-interaction and interaction periods. Valence, arousal, and the positive affect of the human participant significantly increased during interaction with the canine participant. Also, we observed in our pilot study that (a) the canine heart rate was more dynamic than the human's during interactions, (b) the surveys proved to be the best indicator of the subjects' affective state, and (c) the behavior coding approaches best tracked the bond quality between the interacting dyads. Notably, we found that most of the interaction sessions were characterized by extended neutral periods with some positive and negative peaks, where the bonded pairs might display decreased behavioral synchrony. We also present three new representations of the internal and overall dynamics of CAIs for adoption by the broader field. Lastly, this paper discusses ongoing options for further dyadic analysis, interspecies emotion prediction, integration of contextually relevant environmental data, and standardization of human-animal interaction equipment and analytical approaches. Altogether, this work takes a significant step forward on a promising path to our better understanding of how CAIs improve well-being and how interspecies psychophysiological states can be appropriately measured.

Relationship Between Heart Rate and Perceived Stress in Intensive Care Unit Residents: Exploratory Analysis Using Fitbit Data.

Intensive care unit (ICU) residents are exposed to high stress levels due to the intense nature of their work, which can impact their mental health and job performance. Heart rate measured through wearable devices has the potential to provide insights into residents' self-reported stress and aid in developing targeted interventions. This exploratory study aims to analyze continuous heart rate data and self-reported stress levels and stressors in ICU residents to examine correlations between physiological responses, stress levels, and daily stressors reported. A secondary data analysis was conducted on heart rate measurements and stress assessments collected from 57 ICU residents over a 3-week period using Fitbit Charge 3 devices. These devices captured continuous physiological data alongside daily surveys that assessed stress levels and identified stressors. The study used Spearman rank correlation, point-biserial correlation analysis, 2-tailed paired t tests, and mixed-effect models to analyze the relationship between heart rate features and stress indicators. The findings reveal complex interactions between stress levels and heart rate patterns. The correlation analysis between stress levels and median heart rate values across different percentile ranges showed that lower percentile heart rates (bottom 5%, 10%, 25%, and 50%) had modest correlations with stress, whereas higher percentiles (top 50%, 25%, 10%, and 5%) did not correlate significantly (all P>.05). The 2-tailed paired t test indicated significant differences in stress levels reported in midday versus end-of-day surveys (P<.001), although these changes in stress levels were not consistently reflected in heart rate patterns. Additionally, we explored and found that stressors related to "other health" issues had the highest positive correlation with stress level changes from midday to end-of-day surveys. However, the weak effect of these stressors on peak heart rate suggests that their impact on physiological measures like heart rate is not yet clear. According to our mixed-effects model, stress levels significantly influenced heart rate variations when hierarchical data were taken into account (P=.03), meaning that as the stress level increased, there was a significant increase in mean heart rate. This study highlights the complexity of using heart rate as an indicator of stress, particularly in high-stress environments like the ICU. Our findings suggest that while heart rate is found to correlate with self-reported stress in the mixed-effect model, its impact is modest, and it should be combined with other physiological and psychological measures to obtain a more accurate and comprehensive assessment of residents' stress levels.

Empowering Dementia Communication via Virtual Reality AI-Driven Simulations

The use of effective communication skills is essential for direct care workers to provide quality care for persons living with dementia (PLWD) in long-term service and support settings (LTSS). However, direct care workers often report feeling unprepared, lacking competence in care, and fear interacting with PLWD. This proof-of-concept study proposes Virtual reality Communication Training Optimizing Real-world Interactions (VICTORI) intervention, aiming to develop and evaluate the usability, acceptability, and preliminary effectiveness of virtual reality (VR) simulated communication training for direct care workers using artificial intelligence (AI)-generated patients with dementia. Care-related and social communication scenarios in dementia care will be developed, based on current literature, our validated communication instrument, and AI technology. Six modules consisting of pre-briefing, simulation facilitation, and de-briefing will be developed, refined, and evaluated based on an advisory board of experts and direct care workers’ quantitative and qualitative feedback. To test the usability, acceptability, and effectiveness of the VICTORI intervention, we will use a single group pre- and post-test intervention design, integrating quantitative measures and qualitative semi-structured interviews in this mixed-methods study. Thirty direct care workers will be recruited at two LTSS in North Texas. Direct care workers will participate in six VR-AI simulation commu-nication training sessions that include communication with AI-generated patients, real-time feedback, and detailed evaluation of communication behaviors. Each simulation will be recorded to further evaluate the partici-pant’s communication skills using the Dyadic Communi-cation Observational coding scheme in Dementia care (DCODE). Additionally, participants' potential side effects during VR simulation will be assessed using continuous physiological data and self-reported questionnaires at each simulation session. Communication knowledge and com-petence in dementia care will be assessed before and after training using self-reported questionnaires. Direct care workers will also be interviewed to assess acceptability and satisfaction with the VICTORI training. This proposal will introduce innovative training methods that leverage AI technology into communication training in dementia care. Objective assessments, including AI-based feedback, physiological data, and observational assessments, ensure the reliability and validity of the data to evaluate plausibility of VICTORI intervention.

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes.

Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learning approaches for nocturnal hypoglycemia prediction for different prediction horizons. Studies focusing on nocturnal hypoglycemia prediction in children are scarce, and none, to the best knowledge of the authors, investigate the effect of previous physical activity. The primary objective of this work was to assess the risk of hypoglycemia throughout the night (prediction horizon 9 h) associated with physical activity in children with T1D using data from a structured setting. Continuous glucose and physiological data from a sports day camp for children with T1D were input for logistic regression, random forest, and deep neural network models. Results were evaluated using the F2 score, adding more weight to misclassifications as false negatives. Data of 13 children (4 female, mean age 11.3 years) were analyzed. Nocturnal hypoglycemia occurred in 18 of a total included 66 nights. Random forest using only glucose data achieved a sensitivity of 71.1% and a specificity of 75.8% for nocturnal hypoglycemia prediction. Predicting the risk of nocturnal hypoglycemia for the upcoming night at bedtime is clinically highly relevant, as it allows appropriate actions to be taken-to lighten the burden for children with T1D and their families.

Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.

Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups. Using a longitudinal observational study design, continuous physiologic data were collected on participants with acute pain receiving opioid analgesia. Monitoring continued throughout hospitalization and for up to 7 days posthospital discharge. Opioid administration data were obtained from electronic health record (EHR) and participant self-report. Participants were classified as belonging to 1 of 3 categories based on opioid use history: naïve, occasional, or chronic use. Thirty features were derived from sensor data, and an additional 9 features were derived from participant demographic and treatment characteristics. Physiologic feature behavior immediately postopioid use was compared among naïve and chronic participants, and subsequently features were used to generate machine learning models which were validated using cross-validation and holdout data. Forty-one participants with a combined total of 169 opioid administrations were ultimately included in the final analysis. Four interpretable decision tree-based machine learning models with 14 sensor-based and 5 clinical features were developed to predict class membership on the level of a given observation (dose) and on the participant level. Ranges for model metrics on the participant level were as follows: accuracy 70% to 90%, sensitivity 67% to 100%, and specificity 67% to 100%. Wearable sensor-derived digital biomarkers can be used to predict opioid use status (naïve versus chronic) and the differentiating features may be detecting opioid dependence. Future work should be aimed at further delineating the phenomenon identified in these models (including opioid dependence and/or withdrawal) and at identifying transition states where an individual changes from 1 profile to another with repetitive opioid exposure.

Nanofibrous metal oxide semiconductor for sensory face masks

Sensory face masks are designed to integrate sensors that monitor health metrics, such as breathing rate and body temperature, thereby providing continuous physiological data. However, current sensing materials are predominantly in film form. In this study, we report on the use of fibrous amorphous indium gallium zinc oxide, which offers an enlarged surface area to facilitate gas diffusion and transport into charge-carrying sites. A detailed morphological analysis and an electrical investigation of this fibrous network were conducted. By integrating this material with a wireless, miniaturised flexible circuit and mounting it on a face mask, we successfully monitored different human respiratory states in real time. This wearable face mask demonstrates the potential for promising applications in health monitoring.

Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins

Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient’s circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.

Technical article: Overview of hospital-based data capture systems that acquire continuous ECG and physiologic data

Data capture systems that acquire continuous hospital-based electrocardiographic (ECG) and physiologic (vital signs) data can foster robust research (i.e., large sample sizes from consecutive patients). However, the application of these systems and the data generated are complex and requires careful human oversight to ensure that accurate and high quality data are procured. This technical article will describe two different data capture systems created by our research group designed to examine false alarms associated with alarm fatigue in nurses. The following aspects regarding these data capture systems will be discussed: (1) history of development; (2) summary of advantages, challenges, and important considerations; (3) their use in research; (4) their use in clinical care; and (5) future developments.

Non-invasive pulse arrival time is associated with cardiac index in pediatric heart transplant patients with normal ejection fraction

Objective. Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction (EF). Approach. Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson’s correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and EF. Main Results. Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7 ± 5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8 ± 17.0 beats per minute. The average EF was 54.4 ± 9.6%. The average CI was 3.51 ± 0.72 l min−1 m−2, with ranging from 2.6 to 4.77 l min−1 m−2. The average PAT was 0.31 ± 0.12 s. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57, p < 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p = 0.35) and 0.03 (p = 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p < 0.01). Significance. This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated.

Evaluation of a digital tool for detecting stress and craving in SUD recovery: An observational trial of accuracy and engagement

BackgroundDigital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery. MethodsThis was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection. ResultsSixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected. ConclusionsFuture work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact.

Maintenance and clinical appraisal of data capture systems that acquire continuous hospital-based ECG and physiologic data

Maintenance and clinical appraisal of data capture systems that acquire continuous hospital-based ECG and physiologic data

Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population

Determination of prognosis in the triage process after traumatic brain injury (TBI) is difficult to achieve. Current severity measures like the Trauma and injury severity score (TRISS) and revised trauma score (RTS) rely on additional information from the Glasgow Coma Scale (GCS) and the Injury Severity Score (ISS) which may be inaccurate or delayed, limiting their usefulness in the rapid triage setting. We hypothesized that machine learning based estimations of GCS and ISS obtained through modeling of continuous vital sign features could be used to rapidly derive an automated RTS and TRISS. We derived variables from electrocardiograms (ECG), photoplethysmography (PPG), and blood pressure using continuous data obtained in the first 15 min of admission to build machine learning models of GCS and ISS (ML-GCS and ML-ISS). We compared the TRISS and RTS using ML-ISS and ML-GCS and its value using the actual ISS and GCS in predicting in-hospital mortality. Models were tested in TBI with systemic injury (head abbreviated injury scale (AIS) ≥ 1), and isolated TBI (head AIS ≥ 1 and other AIS ≤ 1). The area under the receiver operating characteristic curve (AUROC) was used to evaluate model performance. A total of 21,077 cases (2009–2015) were in the training set. 6057 cases from 2016 to 2017 were used for testing, with 472 (7.8%) severe TBI (GCS 3–8), 223 (3.7%) moderate TBI (GCS 9–12), and 5913 (88.5%) mild TBI (GCS 13–15). In the TBI with systemic injury group, ML-TRISS had similar AUROC (0.963) to TRISS (0.965) in predicting mortality. ML-RTS had AUROC (0.823) and RTS had AUROC 0.928. In the isolated TBI group, ML-TRISS had AUROC 0.977, and TRISS had AUROC 0.983. ML-RTS had AUROC 0.790 and RTS had AUROC 0.957. Estimation of ISS and GCS from machine learning based modeling of vital sign features can be utilized to provide accurate assessments of the RTS and TRISS in a population of TBI patients. Automation of these scores could be utilized to enhance triage and resource allocation during the ultra-early phase of resuscitation.

Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness.

Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term "manifestations," as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.

Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

Recognition of human mood, alertness and comfort under the influence of indoor lighting using physiological features

Light is an essential and vital component of indoor environments, and it has both visual and non-visual effects on the human body. Illuminance and correlated colour temperature (CCT) have been reported to be closely related to human mood, alertness, and comfort. Recent research has focused on subjective feelings, test results, and transient physiological indicators; however, few studies have used continuous physiological data to explore the physiological features along with light parameters associated with subjective feelings. In this paper, we conducted light experiments at six illuminance levels of functional lighting (300, 400, 500, 600, 700, and 800 lx) and four typical CCT levels (3000, 4000, 5000, and 6000 K) to analyse the light effects on users’ mood, alertness, and visual comfort. The results showed that both illuminance and CCT significantly affected subjective perception. Notably, subjective feelings under the influence of the light environment were evaluated using the CatBoost method through 41 columns of physiological features extracted from electrocardiogram (ECG), electrodermal activity (EDA), skin temperature (SKT) and blood oxygen saturation (SpO2) data. The average accuracy values of the recognition framework for binary classification proposed in this study were 97.4% for the valence of mood, 95.7% for the arousal of mood, 97.1% for alertness, and 97.3% for visual comfort (all standard deviations were less than 2.5%). The research results can help intelligent buildings sense changes in human feelings and adjust control systems to construct human-centred indoor environments for work and study, as well as provide a reference of physiological features for personalised user experience research.

Personal comfort models in long-term monitoring using physiological data from wearable sensors

Personal Comfort Models (PCMs) propose a new approach for human-centric comfort studies overcoming the one-size-fits-all of the conventional models. This research addresses the development of PCMs based on a seven-month long-term monitoring campaign including continuous environmental and physiological data collection through wearables and daily survey submission about subjects’ sensations. To tackle the influence of subjects’ environmental exposure history, time series of environmental data of different durations were used to predict individuals’ perception via Machine Learning models with Support Vector Machine and Random Forest methods. The accuracy and F1-score values of seven different PCMs were confronted for each subject and for the whole group (nine people). The number of datapoints per subject and their answers’ consistency during time affected the models’ accuracy, and the inclusion of physiological signals improved the models’ performance. When considering the whole dataset, the comfort model accuracy decreases supporting that individual subjectivity have an important impact in the environmental perception prediction.