Heart Rate Data Research Articles

A feasibility study to explore the use of real‐time caregiver reports for people living with dementia at home to label multi‐modal sensor data

AbstractBackgroundMulti‐modal sensor systems, consisting of sensors throughout the home, are able to collect data on the person living with dementia’s (PlwD’s) activities. However, caregivers may have challenges interpreting the data, leading to a lack of actionable information. It’s important to get real‐time reports from caregivers to label the sensor data to build relevant machine learning algorithms; yet, this has not been done with PlwD. The aim of this feasibility study was to explore if the use of a multi‐modal sensor system in conjunction with real‐time caregiver reports can provide valuable insights into the daily lives of PlwD.MethodWe developed the MAISON (Multimodal AI‐based Sensor platform for Older Individuals) system and deployed it into the homes of dyads (PlwD and their family caregiver) for 8‐weeks. MAISON includes: a smartphone, a smartwatch to continuously collect heart rate, step, and accelerometer data to infer walking, and running, as well as a smart mattress that can collect sleep time and quality. A digital diary was added to the MAISON system that automatically sends the caregiver 2‐3 pre‐set questions every day about their loved one. Caregivers could also report any significant occurrences throughout the day, as they see fit, through the app.ResultTwo dyads were recruited to date. Only about 15% of data was missing from the sensor system by the end of the study. Caregivers were providing their reports with high adherence. The caregivers’ reports were used to label the sensor data successfully, and upon analysis, we found that caregivers’ reports were reflected in the sensor data and the labels derived from their reports were accurate. Results indicate this study methodology is an innovative approach to utilize the family caregiver’s reports.ConclusionThis is the first longitudinal study to demonstrate the feasibility of a multi‐modal sensor system used by caregivers and PwlD. Findings show that caregivers' reports can be useful sources of data for labeling sensor data. The conjunction of caregiver reports and sensor data will provide valuable information to build accurate machine learning models about behaviors of PlwD which is a foundational step to support dyads at home using technology.

A machine learning-assisted system to predict thyrotoxicosis using patients’ heart rate monitoring data: a retrospective cohort study

Previous studies have shown a correlation between resting heart rate (HR) measured by wearable devices and serum free thyroxine concentration in patients with thyroid dysfunction. We have developed a machine learning (ML)-assisted system that uses HR data collected from wearable devices to predict the occurrence of thyrotoxicosis in patients. HR monitoring data were collected using a wearable device for a period of 4 months in 175 patients with thyroid dysfunction. During this period, 3 or 4 thyroid function tests (TFTs) were performed on each patient at intervals of at least one month. The HR data collected during the 10 days prior to each TFT were paired with the corresponding TFT results, resulting in a total of 662 pairs of data. Our ML-assisted system predicted thyrotoxicosis of a patient at a given time point based on HR data and their HR-TFT data pair at another time point. Our ML-assisted system divided the 662 cases into either thyrotoxicosis and non-thyrotoxicosis and the performance was calculated based on the TFT results. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of our system for predicting thyrotoxicosis were 86.14%, 85.92%, 52.41%, and 97.18%, respectively. When subclinical thyrotoxicosis was excluded from the analysis, the sensitivity, specificity, PPV, and NPV of our system for predicting thyrotoxicosis were 86.14%, 98.28%, 94.57%, and 95.32%, respectively. Our ML-assisted system used the change in mean, relative standard deviation, skewness, and kurtosis of HR while sleeping, and the Jensen–Shannon divergence of sleep HR and TFT distribution as major parameters for predicting thyrotoxicosis. Our ML-assisted system has demonstrated reasonably accurate predictions of thyrotoxicosis in patients with thyroid dysfunction, and the accuracy could be further improved by gathering more data. This predictive system has the potential to monitor the thyroid function status of patients with thyroid dysfunction by collecting heart rate data, and to determine the optimal timing for blood tests and treatment intervention.

Effect of age and flying experience on heart rate response of fighter aircrew during high-G exposure in the high-performance human centrifuge

Objectives: Institute of Aerospace Medicine Indian Air Force regularly conducts high-G training in the human rated high-performance human centrifuge (HPHC) for fighter aircrew. It was hypothesized that the cardiovascular response of young and inexperienced pilots who undergo training in the HPHC may be different from the elder and experienced pilots as age and flying experience may have some effect on the CVS response to high-G exposure. Material and Methods: A retrospective analysis of the heart rate data from the data bank of the department of acceleration physiology and spatial orientation was done to understand differences in heart rate response between young and older fighter aircrew. Results: A total of 624 successful HPHC runs were evaluated for the baseline heart rate (before high-G exposure), peak heart rate (PHR) (during the exposure), and heart rate after the exposure of the run. The mean age, height, and weight of the subjects were 27.62 ± 5.5 years, 175.31 ± 4.8 cm, and 72.81 ± 8.4 kg, respectively. Student’s t-test revealed that there was a significant difference in the basal heart rate and PHR during the high-G exposure between young, inexperienced, and older experienced pilots. Higher basal heart rate and PHR during high-G exposure among younger pilots could be explained by anxiety due to inexperience and a tendency to pull harder in comparison to other pilots who with experience tend to be more adjusted and pull slower to meet the desired G-level during the high-G training. Conclusion: The cardiovascular response during exposure to a high-G environment is significantly different between young, inexperienced pilots, and other senior pilots.

Ivabradine in the Prevention, and Reduction in Size, of Perioperative Myocardial Injury in Patients Undergoing Orthopedic Surgery for Acute Fracture.

Perioperative myocardial injury is common after major noncardiac surgery and is associated with adverse outcomes. This study investigated the use of ivabradine in patients undergoing urgent surgery for fracture. This was a prospective, double-blind, placebo-controlled, randomized clinical trial. Participants were enrolled 1:1 into ivabradine or placebo arm, and study drug was commenced before operation and continued for 7 days or until discharge. High-sensitivity troponin I was measured daily using Abbott Alinity analyzer and assay, and heart rate data were obtained using continuous Holter monitoring. A total of 199 patients underwent acute orthopedic surgery, 98 in the ivabradine group and 101 in the placebo group. The mean age was 78.7 years (range, 77.5-79.9 years), with 68% women. The average heart rate was 5 to 11 beats per minute lower in the ivabradine group compared with the placebo group at all time points (P<0.001 for all). There was no statistically significant difference between the ivabradine and placebo groups in the number of patients who had perioperative myocardial injury: 28.6% versus 31.6% (P=0.71). In patients with perioperative myocardial injury, average peak troponin was 168.8 ng/L (±431.2 ng/L) in the ivabradine group and 2094.5 ng/L (±7201.9 ng/L) in the placebo group (P=0.16). There was no statistically significant difference between groups in 30-day mortality, blood pressure, stroke, or major adverse cardiovascular event. Starting ivabradine preoperatively in elderly patients requiring acute surgery for fracture did not result in a statistically significant difference in the incidence of perioperative myocardial injury. There was no statistically significant difference in morbidity, mortality, or adverse events between treatment groups. URL: https://www.anzctr.org.au/; Unique identifier: ACTRN12616001634460p.

The effects of massage and white noise on physiological parameters of preterm infants: A randomized controlled research

PurposeThe present research was conducted to find out the effects of massage and white noise on physiological parameters in preterm infants. Material-methodWhile each preterm infant in the massage group was given massage by the researcher in the morning and evening for three days, the infants in the white noise group listened to white noise in the morning and evening for three days. Standard nursing care was provided for three days in the control group. Physiological parameters of the preterm infants in all of the groups on the first, second and third days were recorded by the researcher before the procedure. Following the interventions to preterm infants by the researcher, peak heart rate, respiratory rate and oxygen saturation data were recorded. ResultsSignificant difference was found between the first, second and third day peak heart rate, respiratory rate and oxygen saturation values of the preterm infants in the massage and white noise group when compared with the control group. No significant difference was found between the white noise and massage group. In the temporal analysis of the massage and white noise group, a decrease was found in peak heart rate and respiratory rate, while an increase was found in oxygen saturation values. ConclusionMassage and white noise were found to be effective on physiological parameters of preterm infants. It was found that in the neonatal intensive care unit, nurses can prefer massage or white noise methods in healing physiological parameters of preterm infants.

Study of special physical fitness of juniors of 17-19 years old

The article analyzes the results of special physical preparation of 17-19 years old juniors in the quadruple test uphill 300 m. The data of heart rate on the background of the physical test are disclosed. The aim of the work To determine the indicators of special preparation of 17-19 years old juniors. Result. We defined that indicator of the total time of 4 races changed by 4,9 s (t=4,13; p&lt;0,01), by 2,1 s (t=2,42; p&lt;0,01) of time of passing of a rise in the 1st race, by 5,6 % (t=5,46; p&lt;0,001) of speed of passing of a rise in the 4th race concerning the first. Indicators of physical qualities of juniors of 17-19 years old during the specially-preparatory stage had a statistical difference in indicators of the Cooper's test 12-minute run (t=12,61; p&lt;0,001), in a trunk lift in a saddle for 1 minute (t=4,99; p&lt;0,01), whereas positive dynamics was obtained in the indicators of flexion-extension of arms in a lying down support from the floor, in a long jump from a place, in pulling up on a crossbar (p&gt;0,05).

Intelligent Estimation of Exercise Induced Energy Expenditure Including Excess Post-Exercise Oxygen Consumption (EPOC) with Different Exercise Intensity.

The limited availability of calorimetry systems for estimating human energy expenditure (EE) while conducting exercise has prompted the development of wearable sensors utilizing readily accessible methods. We designed an energy expenditure estimation method which considers the energy consumed during the exercise, as well as the excess post-exercise oxygen consumption (EPOC) using machine learning algorithms. Thirty-two healthy adults (mean age = 28.2 years; 11 females) participated in 20 min of aerobic exercise sessions (low intensity = 40% of maximal oxygen uptake [VO2 max], high intensity = 70% of VO2 max). The physical characteristics, exercise intensity, and the heart rate data monitored from the beginning of the exercise sessions to where the participants' metabolic rate returned to an idle state were used in the EE estimation models. Our proposed estimation shows up to 0.976 correlation between estimated energy expenditure and ground truth (root mean square error: 0.624 kcal/min). In conclusion, our study introduces a highly accurate method for estimating human energy expenditure during exercise using wearable sensors and machine learning. The achieved correlation up to 0.976 with ground truth values underscores its potential for widespread use in fitness, healthcare, and sports performance monitoring.

Multitask Learning for Automated Sleep Staging and Wearable Technology Integration

Scoring sleep stages is an essential procedure for the diagnosis of sleeping disorders. Conventional sleep staging is a laborious and costly procedure requiring multimodal biological signals and an expert for the assessment. There has always been a demand for approaches which can exempt the need to going through diagnostic procedures under specialized facilities and enable automated sleep staging. Herein, a high‐performance multitask learning model enabling high‐accuracy sleep staging using heart rate data is reported. The proposed algorithm exhibits superior performance with reduced computational resource in comparison with the competing machine and deep learning algorithms when trained and evaluated using electrocardiography and photoplethysmogram (PPG) data. The reported model consumes ≈7.5 times less training parameters and ≈75% less amount of input data than the previously reported models and yields better or comparable performance (mean per night accuracy of 77.5% and Cohen's kappa of 0.643). To demonstrate its potential for wearable electronics, the reported algorithm is implemented in a fully integrated watch. The reported integrated watch is a stand‐alone fully functional platform, which automatedly captures PPG data from the subject's wrist, predicts sleep stages, and displays the result on a screen as well as an associated smartphone application.

Evaluation and application of treadmill Cardiopulmonary Exercise Testing and field measurement results of female national team water polo players

Abstract Introduction Due to intensive sport adaptation, examinations of elite athletes are a special entity of cardiology. Cardiovascular follow-up of elite athletes is a significant method of supporting safe and successful sport. In many sports like water polo, training work takes place in different environments and with different forms of movement, then the tests applied in laboratory circumstances. Although treadmill cardiopulmonary exercise testing (CPET) is widely used in cardiology screening, we have little information of its usefulness in physical fitness follow-up in non-running sports. Purpose We aimed to analyze and to compare the data of elite youth team (YT) and adult water polo players collected from maximal intensity cardio-pulmonary exercise testing (CPET) and field examinations in definition of current fitness status of the different age groups. Methods During the age- and sport specific field measurements (swimming tests) and CPET examinations, we collected resting and exercise heart rate data and lactate levels at defined time points. During CPET, continuous 12-lead ECG, gas analysis and repeated blood pressure measurements were also carried out. Results 46 female water polo players were examined (age: 18.5±5.9y, adults: 19, training: 18.0±7.9h/w). Comparing CPET and field measurements, no differences were found between resting heart rate (75.7±13.4vs73.1±13.0 BPM, p=0.22) and resting lactate levels (1.5±0.4vs1.6±0.6 mM, p=0.29). Achieved maximal heart rates were higher during CPET examinations, than during field tests (196.3±9.7 vs 191.0±12.5 BPM, p=0.001) On the contrary, maximal lactate levels were lower during CPET in comparison to field measurements (8.4±2.4 vs 9.6±2.7 mM, p=0.01). Lactate levels stayed high after routine cool-down swimming ordered by the trainers, but decreased after suggested further training (6.6±2.7 vs 3.7±1.6 mM, p=10-16). When comparing the age groups, CPET examinations showed similar relative maximal aerobic capacity (44.4±4.8vs44.9±5.5 ml/kg/min, p=0.72) and peak lactate values (8.2±2.7vs8.7±1.9 mM, p=0.48), while maximal heart rates were higher in YT groups (200.7±7.2 vs 190.1±9.6 BPM, p&amp;lt;0.001) as compared to adults. Similarly, maximal lactate levels showed no differences (9.1±2.6 vs 10.4±2,7 mM, p=0.13), while maximal heart rates were higher in YT groups (197.7±10.0 vs 181.4±8.9 BPM, p&amp;lt;0.001) as compared to adults during field examinations. Conclusions Although field measurements and laboratory CPET results differ from several points of view, differences between age groups and the kinetics of the data are similar. Maximal heart rates are easier to achieve during CPET, while more intensive lactate increase could be observed during field measurements. Combining the above examinations helps defining maximal physiological values, fitness status, differences between different age groups, and optimizing training plans, as well as differentiating between physiological and pathological sport adaptation.

Heart Rate Monitoring, Blood Oxygen Levels and Location Determination for Covid 19 Patients Using Internet of Things Technology

The COVID-19 virus is very dangerous. In addition to attacking the lungs, this virus can also attack the heart directly. The relationship between heart health and COVID-19 occurs because blood vessel clots in COVID-19 patients increase the risk of blood vessel disorders and make the heart work harder. Therefore, researchers designed a heart rate monitoring device. This tool serves to maintain heart health and oxygen levels in the patient's blood and can also monitor people under COVID surveillance. The formulation of the problem from this research is how to design an IoT system to monitor COVID-19 patients through heart rate and location and how it performs. The purpose of this study is to analyze the performance of the device and design a tool to measure heart rate and blood oxygen levels through the IoT system and GPS location so that doctors can access heart rate data from any location. This research method uses a Gy-MAX30100 sensor, Wemos, and a GPS module. Of the 2 data samples that have been tested, the highest heart rate value is 78 bpm, the lowest is 76 bpm, and the highest oxygen level is 95 mmHg and the lowest is 93 mmHg. So in conclusion, this tool can make it easier for doctors to get important information about the condition of patients or people under observation that can be accessed by doctors anywhere and anytime via the internet.

Children can rate perceived effort but do not follow intensity instructions during soccer training.

The perception of effort is elementary for the self-regulation of exercise intensity in sports. The competence for rating perceived effort (RPE) seems to be related to physical and cognitive development. Children accurately rate perceived effort during incremental exercise tests when loads progressively increase, but it remains unclear how children perform when they participate in sports games, which are characterized by complex tasks with varying intensity profiles. The present study investigates children's competencies for rating perceived effort and producing predetermined intensities during soccer training. Twenty-five children aged 11-13 years performed two similar training sessions. In the first session, the children trained without intensity instructions and continuously rated their effort. In the second session, the children were instructed to produce predefined intensities. Before the first training session, executive functions were assessed by cognitive performance tests and a self-report measure. RPE correlated significantly with heart rate measures (R2 = 0.27, p < 0.001). As confirmed by factor analysis, individual differences in these correlations were related to the outcomes of the cognitive tests and the self-report measure. RPE in training session 2 differed from RPE in training session 1 (d = 1.22, p < 0.001), although the heart rate data did not differ significantly between training sessions (d = -0.19, p = 0.780). Thirteen-year-old children performed significantly better than eleven-year-old children (d = 1.69, p = 0.027). The results suggest that children are able to rate perceived effort during soccer training and that this ability is related to executive functions. Conversely, children may not be able to alter their intensities in response to instructions, although their ratings suggest that they have largely succeeded in doing so.

Predicting Vaso-Occlusive Crises in Sickle Cell Disease through Digital, Longitudinal Tracking of Wearable Metrics and Patient-Reported Outcomes

Background Sickle Cell Disease (SCD) is a genetic disorder caused by an HBB gene mutation and resulting structural variant haemoglobin (HbS) that polymerises upon deoxygenation, leading to a sickled morphology of red blood cells ( Chakravorty et al., Arch Dis Child 2015). This causes acute pain episodes, known as vaso-occlusive crises (VOC), that can result in severe end-organ damage in the long-term and a shortened lifespan ( Jang et al., J Transl Med 2021). Responsible for an estimated 95% of SCD hospitalisations and as a key predictor of death ( Darbari et al., Eur J Haematol 2020, Ballas et al., Am J Hematol 2005), it is critical to find new ways to drive early detection of VOCs in order to support preventative interventions. Aims This work aimed to develop a machine learning (ML) algorithm capable of predicting the potential onset of a VOC, using physiological data captured longitudinally by a wearable smartwatch, as well as patient-reported outcomes (PROs) entered via a specialised mobile phone application. Methods Following informed patient consent, participants gained access to a mobile phone application, or “digital patient wallet”, encompassing their health data and access to a PRO entry portal ( Summers et al., HemaSphere 2023). This allowed for daily recording of PROs including EQ-5D-5L, pain, mood, and fatigue scores, alongside self-reported VOCs. Participants were also provided with a Withings ScanWatch to be worn day and night, capturing physical activity, sleep quality, and heart rate data in an automated manner. Medical record data including healthcare utilisation, pathology, and demographic data was obtained through participant completion of a Subject Access Request form. Data for a snapshot cohort of 186 patients over a 1-month period (May-June 2023) were analysed through an ensemble of ML models: gradient boosting machine, neural network, and k-means clustering. All collated metrics were fed into the model, with the exclusion of hospitalisation as an 'a posteriori' variable, to ensure predictions were based on appropriate variables and not strengthened by self-confirming factors. The resulting predicted likelihood of a VOC for each participant was calculated on each day and given as a scale from 0-100%, with a likelihood of 75% or higher deemed to be a predicted VOC. This was then assessed against the true incidence of the participants' self-reported VOCs. Results The mean age was 36 (SD 11.6) years, and most patients were female (70%). The HbSS genotype was most common (75%), followed by HbSC (16%), and HbS Beta + Thalassemia (5%). 82,804 datapoints were collated for all participants across this period, encompassing 90 different variables. 69 instances of a VOC were self-reported by patients during the period of analysis, 58 (84%) of which were accurately predicted by the algorithm. 2,366 instances of no VOC were self-recorded, giving a negative predicted value of 83% (1,958/2,366 predicted). Further breakdown by the predicted likelihood score is displayed in Figure 2, highlighting the spread of individually calculated VOC likelihood scores per day against the incidence of patient-reported VOCs. Summary/Conclusions This work provides an important translation of daily trackable metrics through a remote, digital ecosystem, to the real-world prediction of potential VOC events in SCD. The purpose of this algorithm is, at its core, to act as a pre-emptive warning system from which proactive behavioural changes or interventions can be enacted. As such, the prediction of additional potential risk scores above the 75% threshold reflects an approach of ‘over-predicting’ considered to be a preferable strategy for the algorithm, given the severe nature of VOCs and the health impacts of missing a potential event. Our ongoing aim is to further develop the algorithm and its contributing metrics against medical data, in addition to patient self-reported events, to further optimise the predictor. This includes the curation of core metrics requiring minimal patient input, in order to reduce missing datapoints across those that feed into the algorithm and thereby reduce unpredicted VOCs. Ultimately, we hope to provide an early alert system that will help patients to identify their VOCs sooner, and thus action the next steps that may prevent possible hospital admissions.

47 Assessing the Feasibility of Cardiorespiratory Exercise During Functional MRI

Objective:To examine the feasibility of implementing a cardiorespiratory exercise stimulus during functional Magnetic Resonance Imaging (fMRI).Participants and Methods:12 young adults (age: 18-22 years) completed progressive maximal exercise testing and a brain MRI scan. During scanning, participants completed three runs of functional MRI (volumes = 619; TR = 800 ms; multiband = 4; voxel size = 3 mm3). During each 8 minute fMRI run, participants completed an exercise challenge consisting of alternating blocks of exercise and rest. Exercise was implemented with a cardiostepper, an MRI-compatible device (similar to a Stairmaster) capable of generating a cardiorespiratory exercise stimulus. During exercise blocks, participants stepped at a rate of 60 Hz with pedal resistance determined by participants' fitness level. Heart rate and respiration data were collected during MRI. fMRI data were processed and analyzed using FMRIB Software Library (FSL). The ARtifact Detection Toolbox (ART) software was also used to identify volumes with significant artifact, and ICA-AROMA was used to remove motion-related BOLD signal components.Results:During exercise blocks, heart rate increased (mean = 131 beats per minute) compared to rest (mean = 87 beats per minute; t(34) = 4.3; p &lt; .001). The mean heart rate during exercise blocks corresponds to an exercise intensity in the light to moderate intensity range for this age group. Motion (median framewise displacement) was significantly higher during exercise (mean = .53 mm) than rest (mean = .36 mm). Across all blocks, ART classified 19.8% of brain volumes as artifact-containing outliers, with 69% of the outliers occurring during exercise blocks. Although greater head motion was observed during exercise, the use of ICA-AROMA reduced the impact of motion considerably, recovering an additional 25% of the task-related signal, relative to noise. Comparison of fMRI activity during exercise versus rest revealed significant associations with primary and supplementary motor cortices, hippocampus, and the insula, among other regions.Conclusions:The current study demonstrates the feasibility of eliciting light to moderate intensity cardiorespiratory exercise (using a lower body stepping exercise) during functional MRI. Although increased head motion was observed during exercise compared to rest, the degree of head motion was roughly approximate to the values published in previous fMRI studies and post image acquisition processing improved task-related signal. During exercise, increased brain activation was observed in regions associated with the central command network, which regulates autonomic nervous system and musculoskeletal function during exercise.

Effect of Propofol on Heart Rate and Its Coupling to Cortical Slow Waves in Humans.

Propofol causes significant cardiovascular depression and a slowing of neurophysiological activity. However, literature on its effect on the heart rate remains mixed, and it is not known whether cortical slow waves are related to cardiac activity in propofol anesthesia. The authors performed a secondary analysis of electrocardiographic and electroencephalographic data collected as part of a previously published study where n = 16 healthy volunteers underwent a slow infusion of propofol up to an estimated effect-site concentration of 4 µg/ml. Heart rate, heart rate variability, and individual slow electroencephalographic waves were extracted for each subject. Timing between slow-wave start and the preceding R-wave was tested against a uniform random surrogate. Heart rate data were further examined as a post hoc analysis in n = 96 members of an American Society of Anesthesiologists Physical Status II/III older clinical population collected as part of the AlphaMax trial. The slow propofol infusion increased the heart rate in a dose-dependent manner (mean ± SD, increase of +4.2 ± 1.5 beats/min/[μg ml-1]; P < 0.001). The effect was smaller but still significant in the older clinical population. In healthy volunteers, propofol decreased the electrocardiogram R-wave amplitude (median [25th to 75th percentile], decrease of -83 [-245 to -28] μV; P < 0.001). Heart rate variability showed a loss of high-frequency parasympathetic activity. Individual cortical slow waves were coupled to the heartbeat. Heartbeat incidence peaked about 450 ms before slow-wave onset, and mean slow-wave frequency correlated with mean heart rate. The authors observed a robust increase in heart rate with increasing propofol concentrations in healthy volunteers and patients. This was likely due to decreased parasympathetic cardioinhibition. Similar to non-rapid eye movement sleep, cortical slow waves are coupled to the cardiac rhythm, perhaps due to a common brainstem generator.

Evaluating the viewer experience of interactive virtual reality movies

Significant advances in virtual reality (VR) technology have called into question the traditional methods of cinema storytelling and dissemination. New VR devices, such as the Meta (Oculus) Quest, have expanded the possibilities for viewing movies. The purpose of this study is to compare the emotional and cognitive impacts of VR and traditional 2D movies. In this study, sixty volunteers were divided into two groups and presented a movie (Gala) in 2D or VR format. We employed a multimodal method to assess the cognitive and emotional effects of the film both during and after watching. Our technique combined self-reports, interviews, questionnaires, and objective heart rate and EEG brain activity data. After quantitative and qualitative evaluation, it was discovered, that regardless of media, there was a substantial influence of the movie on the emotional state of the participant’s mood. Moreover, compared to the traditional 2D-movie, the VR movie led to more consistent and robust positive effect on all aspects of self-rated affect. The difference in self-reported mood was corroborated by reduced EEG amplitudes in the beta frequency band, indicating higher levels of positive affectivity, which was only observed for the VR movie. Lastly, the VR movie also leads to overall higher self-rated immersion and engagement than the 2D version. Our results highlight the potential of VR movies to engage and emotionally affect audiences beyond traditional cinema. Moreover, our study highlights the value of using a multidisciplinary method for analysing audience impacts.

Impacts of Increasing Temperature on the Metabolism of Confined and Freely Moving American Lobsters (Homarus americanus).

AbstractGulf of Maine waters are warming rapidly, prompting a reevaluation of how commercially important marine species will respond. The goal of this study was to determine the respiratory, cardiac, and locomotory responses of American lobsters (Homarus americanus) to increasing water temperatures and to compare these to similar published studies. First, we measured the heart rate and ventilation rate of 10 lobsters that were confined in a temperature-controlled chamber while exposing them to gradually warming temperatures from 16 to 30 °C over 7 h. Both heart rate and ventilation rate increased along with the temperature up to a break point, with the mean heart rate peaking at 26.5 ± 1.6 °C, while the ventilation rate peaked at 27.4 ± 0.8 °C. In a subset of these trials (n = 5), oxygen consumption was also monitored and peaked at similar temperatures. In a second experiment, both the heart rate and activity of five lobsters were monitored with custom-built dataloggers while they moved freely in a large tank, while the temperature was increased from 18 to 29 °C over 24 h. The heart rate of these lobsters also increased with temperature, but their initial heart rates were lower than we recorded from confined lobsters. Finally, we confirmed that the low heart rates of the freely moving lobsters were due to the methods used by comparing heart rate data from eight lobsters collected using both methods with each individual animal. Thus, while our overall results are consistent with data from previous studies, they also show that the methods used in studies of physiological and behavioral responses to warming temperatures can impact the results obtained.

Noise-Robust Pulse Wave Estimation from Near-Infrared Face Video Images Using the Wiener Estimation Method.

In this paper, we propose a noise-robust pulse wave estimation method from near-infrared face video images. Pulse wave estimation in a near-infrared environment is expected to be applied to non-contact monitoring in dark areas. The conventional method cannot consider noise when performing estimation. As a result, the accuracy of pulse wave estimation in noisy environments is not very high. This may adversely affect the accuracy of heart rate data and other data obtained from pulse wave signals. Therefore, the objective of this study is to perform pulse wave estimation robust to noise. The Wiener estimation method, which is a simple linear computation that can consider noise, was used in this study. Experimental results showed that the combination of the proposed method and signal processing (detrending and bandpass filtering) increased the SNR (signal to noise ratio) by more than 2.5 dB compared to the conventional method and signal processing. The correlation coefficient between the pulse wave signal measured using a pulse wave meter and the estimated pulse wave signal was 0.30 larger on average for the proposed method. Furthermore, the AER (absolute error rate) between the heart rate measured with the pulse wave meter was 0.82% on average for the proposed method, which was lower than the value of the conventional method (12.53% on average). These results show that the proposed method is more robust to noise than the conventional method for pulse wave estimation.

Estimation of cardiorespiratory fitness using heart rate and step count data

Predicting cardiorespiratory fitness levels can be useful for measuring progress in an exercise program as well as for stratifying cardiovascular risk in asymptomatic adults. This study proposes a model to predict fitness level in terms of maximal oxygen uptake using anthropometric, heart rate, and step count data. The model was trained on a diverse cohort of 3115 healthy subjects (1035 women and 2080 men) aged 42 ± 10.6 years and tested on a cohort of 779 healthy subjects (260 women and 519 men) aged 42 ± 10.18 years. The developed model is capable of making accurate and reliable predictions with the average test set error of 3.946 ml/kg/min. The maximal oxygen uptake labels were obtained using wearable devices (Apple Watch and Garmin) during recorded workout sessions. Additionally, the model was validated on a sample of 10 subjects with maximal oxygen uptake determined directly using a treadmill protocol in a laboratory setting and showed an error of 4.982 ml/kg/min. Unlike most other models, which use accelerometer readings as additional input data, the proposed model relies solely on heart rate and step counts—data readily available on the majority of fitness trackers. The proposed model provides a point estimation and a probabilistic prediction of cardiorespiratory fitness level, thus it can estimate the prediction’s uncertainty and construct confidence intervals.

Research on Hybrid Data Clustering Algorithm for Wireless Communication Intelligent Bracelets

Wireless communication smart bracelet data include motion data, sleep time data, heart rate and blood pressure data and positioning data, etc. These data have diversity and high complexity, and there are interconnections or interactions between the data, which have high clustering difficulty. To this end, a new data clustering algorithm is studied for wireless communication smart bracelets. The K-medoids algorithm is used to calculate the intra-cluster, inter-cluster, or overall similarity to complete the initial clustering of the bracelet data. Setting the clustering evaluation index can determine the optimal number of clusters. The data objects that are closely surrounded and relatively dispersed are selected as the initial clustering centers and combined with the new index IXB to complete the improvement of the data clustering algorithm. The test results show that the accuracy, recall, and F1 of the research algorithm for clustering the heart rate monitoring dataset, temperature monitoring dataset, energy consumption dataset, and sleep monitoring dataset are higher than 97%, which indicates that the data clustering effect of the algorithm is good.

A 5G-Enabled and Self-Powered Sensor Data Management Scheme for the Smart Medical Platform System

With the rapid development of information technology and the Internet, the advent of 5G era, combined with the self-powered sensors technology of the Internet of things, can meet the needs of the rapid popularization of intelligent devices and intelligent terminals. With the national economic growth and the continuous development of high-tech industry, the people’s demand for medical services and medical construction is gradually increasing. In order to improve the medical speed, treatment efficiency and treatment success rate of the medical platform, the intelligent medical system has become a system platform widely used in major hospitals. Based on 5G development and self-powered sensors development, this paper studies the construction of intelligent medical platform and system data management. We analyze the development of 5G and the application status of self-powered sensors in the medical field, and briefly describe the defects and problems existing in the intelligent medical system. Use 5G network propagation speed to optimize and improve the intelligent medical system, manage the system data information in the big data environment, and build an information integration platform. In the medical information data collection, the intelligent self-powered sensors technology is used to expand the retrieval range and improve the accuracy and reliability of the system. A set of intelligent medical data retrieval system is designed by using artificial intelligence algorithm and 5G communication technology to realize and optimize the function of patient service on the medical platform. Finally, the data management of medical platform is studied under the data security mechanism. Combined with advanced self-powered sensors technology, the purpose of dynamically monitoring patients’ heart rate and body test data is realized, and the information is transmitted to the medical management platform to help doctors and nurses analyze and diagnose. The results show that 5G technology and intelligent self-powered sensors technology play a good role in promoting the establishment of intelligent medical platform, and improve the overall accuracy and reliability of medical system.