Validity of the Polar H10 Sensor for Heart Rate Variability Analysis during Resting State and Incremental Exercise in Recreational Men and Women

Diurnal variation of heart rate variability in individuals with spinal cord injury

BACKGROUND

Heart rate variability (HRV) may provide objective information about cardiogenic autonomic balance in individuals with spinal cord injury (SCI). The aim of this study was to characterize the diurnal variation of HRV in individuals with SCI at lesion level T6 and above and lesion level below T6.

METHODS

This was a retrospective analysis of a prior cross-sectional study. Individuals with chronic SCI underwent 24 h recording of the time between consecutive R waves (RR interval) to derive parameters of HRV as follows: standard deviation of all normal-to-normal R-R intervals (SDNN) and square root of the mean of the squared differences between successive R-R intervals (RMSSD) (time domain); and high frequency power (HF), low-frequency power (LF), very low frequency power (VLF), ultra-low frequency power (ULF) and total power (TP) (frequency domain). Changes in the magnitude of HRV outcomes over the 24 h period were investigated using a novel multi-component cosinor model constrained to the form of a three-harmonic Fourier series.

RESULTS

Participants were grouped as lesion level T6 and above (n = 22) or below T6 (n = 36). Most of them were male (n = 40, 69%) and the median age (interquartile range) was 50.5 (28) years. Both groups exhibited similar diurnal patterns in most HRV metrics. The lowest values occurred in the late afternoon (4-6 pm) and gradually increased, peaking around midnight to early morning (1-6 am). Exceptions included RMSSD, which peaked before midnight, and ULF, which showed a double peak pattern that peaked from 11 am to 1 pm and 4-6 am in participants with lesion level at T6 and above. The HRV values in participants with lesion level T6 and above were generally lower than participants with lesion level below T6, except for peak values of RMSSD, HF and LF.

CONCLUSION

This study demonstrated substantial diurnal variation of HRV in participants with SCI in both groups of participants. In clinical and research settings, diurnal variations in HRV must be taken into consideration.

4-week stretching program after submaximal strength exercise affects performance but not heart rate variability and lactate clearance. An exploratory study

Previous research has demonstrated that stretching can enhance athletic performance and induce cardiovascular adaptations. This study aims to assess whether a 4-week preventative stretching routine can enhance heart rate variability and heart rate recovery, faster blood lactate clearance, and improve performance following submaximal strength exercises. Twenty-four healthy adults were recruited and randomly allocated to either the experimental group or the control group. Both groups engaged in submaximal strength exercises (5 sets to voluntary failure at 60% of 1RM) comprising bench press and back squat exercises under baseline conditions and after stretching protocol. The experimental group followed the Stretching Protocol, while the control group adhered to their regular training routine. ANOVA analysis revealed a significant pre-post interaction effect between groups in the variable of squat repetitions, although no notable pre- or post-differences were observed in heart rate variability, heart rate recovery, blood lactate concentration, or bench repetitions in either group. A 4-week preventative stretching program does not appear adequate to enhance lactate clearance and cardiovascular adaptation after submaximal strength exercises in resistance-trained individuals compared to the control group. However, it is plausible that such a stretching routine may mitigate muscle fatigue, though further investigation is warranted to substantiate this hypothesis.

Clinical study on the safety and feasibility of AiWalker-K for lower limbs exercise rehabilitation in children with cerebral palsy

BACKGROUND

Robotic-assisted gait training (RAGT) devices are effective for children with cerebral palsy (CP). Many RAGT devices have been created and put into clinical rehabilitation treatment. Therefore, we aimed to investigate the safety and feasibility of a new RAGT for children with CP.

METHODS

This study is a cross-over design with 23 subjects randomly divided into two groups. The occurrence of adverse events and changes in heart rate and blood pressure were recorded during each AiWalker-K training. Additionally, Gross Motor Function Measure-88 (GMFM-88), Pediatric Balance Scale (PBS), 6 Minutes Walking Test (6MWT), Physiological Cost Index, and Edinburgh Visual Gait Score (EVGS) were used to assess treatment, period, carry-over, and follow-up effects in this study.

RESULTS

Adverse events included joint pain, skin pain, and injury. Heart rate and blood pressure were higher with the AiWalker-K compared to the rest (P < 0.05), but remained within safe ranges. After combined treatment with AiWalker-K and routine rehabilitation treatment, significant improvements in 6MWT, GMFM-88 D and E, PBS, and EVGS were observed compared to routine rehabilitation treatment alone (P < 0.05).

CONCLUSIONS

Under the guidance of experienced medical personnel, AiWalker-K can be used for rehabilitation in children with CP.

Inter-day reliability of heart rate complexity and variability metrics in healthy highly active younger and older adults

PURPOSE

To investigate the inter-day reliability of time-domain, frequency-domain, and nonlinear HRV metrics in healthy highly active younger and older adults. The study also assessed the effect of age on the HRV metrics.

METHODS

Forty-four older adults (34 M, 10F; 59 ± 5 years;V ˙ O 2peak  = 40.9 ± 7.6 ml kg-1 min-1) and twenty-two younger adults (16 M, 6F; 22 ± 4 years;V ˙ O 2peak  = 47.2 ± 12.8 ml kg-1 min-1) attended the laboratory. Visit one assessed aerobic fitness through an exercise test. In visits two and three, participants completed a 30-min supine RR interval measurement to derive the HRV metrics.

RESULTS

The younger group (YG) and older group (OG) demonstrated poor to good day-to-day relative and absolute reliability for all HRV metrics (OG, ICCs = 0.33 to 0.69 and between day CVs = 3.8 to 29.2%; YG, ICCs = 0.37 to 0.93 and between day CVs = 3.5 to 36.5%). There was a significant reduction in ApEn (P < 0.001), SampEn (P = 0.031), RMSSD (P < 0.001), SDNN (P < 0.001), LF power (P < 0.001) and HF power (P < 0.001), HRV metrics with ageing. There was no significant effect of age the complexity metrics DFA α1 (P = 0.107), α2 (P = 0.147) and CI-8 (P = 0.493).

CONCLUSION

HRV metrics are reproducible between days in both healthy highly active younger and older adults. There is a decline in linear and nonlinear HRV metrics with age, albeit there being no age-related change in the nonlinear metrics, DFA α1, α2 and CI-8.

Bridging childhood to adulthood: the impact of early life stress on acute stress responses

Background

Childhood trauma exerts enduring impacts on the physical and psychological well-being of individuals in adulthood, influencing their daily functioning. This study aims to investigate the impact of childhood trauma on stress recovery in adults, concentrating on heart rate variations during acute stress exposure.

Methods

A cohort of 126 participants completed the Childhood Trauma Questionnaire (CTQ) and underwent the Trier Social Stress Test (TSST) to elicit acute stress, with continuous heart rate (HR) monitoring for stress recovery assessment.

Results

The results revealed a negative correlation between childhood trauma and stress recovery, prominently observed in instances of emotional neglect and abuse. Individuals with heightened childhood trauma exhibited protracted stress recovery following acute stress exposure.

Conclusion

Childhood traumatic experiences were associated with the recovery from acute stress, as indicated by heart rate indices. These findings contribute to the foundational framework for psychological interventions tailored to individuals with a history of childhood trauma.

Virtual reality to reduce periprocedural anxiety during invasive coronary angiography: rationale and design of the VR InCard trial

INTRODUCTION

Patients undergoing invasive coronary angiography (ICA) experience anxiety due to various reasons. Procedural anxiety can lead to physiological and psychological complications, compromising patient comfort and overall procedural outcomes. Benzodiazepines are commonly used to reduce periprocedural anxiety, although the effect is modest. Virtual reality (VR) is a promising non-pharmacological intervention to reduce anxiety in patients undergoing ICA.

METHODS AND ANALYSIS

A single-centre open-label randomised controlled trial is conducted assessing the effectiveness of add-on VR therapy on anxiety in 100 patients undergoing ICA and experiencing anxiety in a periprocedural setting. The primary outcome is the Numeric Rating Scale (NRS) anxiety score measured just before obtaining arterial access. Secondary outcomes include postarterial puncture and postprocedural anxiety, patient-reported outcome measures (PROMs) of anxiety and physiological measurements associated with anxiety. The NRS anxiety level and physiological measurements are assessed five times during the procedure. The PROM State-Trait Anxiety Inventory and Perceived Stress Scale are completed preprocedure, and the PROM STAI and the Igroup Presence Questionnaire are performed postprocedure.

ETHICS AND DISSEMINATION

The protocol of this study has been approved by the Research Ethics Committee of the Radboud University Medical Centre, the Netherlands (CMO Arnhem-Nijmegen, 2023-16586). Informed consent is obtained from all patients. The trial is conducted according to the principles of the Helsinki Declaration and in accordance with Dutch guidelines, regulations, and acts (Medical Research involving Human Subjects Act, WMO).

REGISTRATION DETAILS

Trial registration number: NCT06215456.

Estimation of physiological exercise thresholds based on dynamical correlation properties of heart rate variability

Aerobic and anaerobic thresholds of the three-zone exercise model are often used to evaluate the exercise intensity and optimize the training load. Conventionally, these thresholds are derived from the respiratory gas exchange or blood lactate concentration measurements. Here, we introduce and validate a computational method based on the RR interval (RRI) dynamics of the heart rate (HR) measurement, which enables a simple, yet reasonably accurate estimation of both metabolic thresholds. The method utilizes a newly developed dynamical detrended fluctuation analysis (DDFA) to assess the real-time changes in the dynamical correlations of the RR intervals during exercise. The training intensity is shown to be in direct correspondence with the time- and scale-dependent changes in the DDFA scaling exponent. These changes are further used in the definition of an individual measure to estimate the aerobic and anaerobic threshold. The results for 15 volunteers who participated in a cyclo-ergometer test are compared to the benchmark lactate thresholds, as well as to the ventilatory threshods and alternative HR-based estimates based on the maximal HR and the conventional detrended fluctuation analysis (DFA). Our method provides the best overall agreement with the lactate thresholds and provides a promising, cost-effective alternative to conventional protocols, which could be easily integrated in wearable devices. However, detailed statistical analysis reveals the particular strengths and weaknessess of each method with respect to the agreement and consistency with the thresholds-thus underlining the need for further studies with more data.

Accuracy of Heart Rate Measurement with Wrist-Worn Wearable Devices in Various Skin Tones: a Systematic Review

BACKGROUND

Wearable consumer technology allows for the collection of a growing amount of personal health data. Through the analysis of reflected LED light on the skin, heart rate measurement and arrhythmia detection can be performed. Given that melanin alters skin light absorption, this study seeks to summarize the accuracy of cardiac data from wrist-worn wearable devices for participants of varying skin tones.

METHODS

We conducted a systematic review, searching Embase, MEDLINE, CINAHL, and Cochrane for original studies that stratified heart rate and rhythm data for consumer wearable technology according to participant race and/or skin tone.

RESULTS

A total of 10 studies involving 469 participants met inclusion criteria. The frequency-weighted Fitzpatrick score for skin tone was reported in six studies (n = 293), with a mean participant score of 3.5 (range 1-6). Overall, four of the ten studies reported a significant reduction in accuracy of heart rate measurement with wearable devices in darker-skinned individuals, compared to participants with lighter skin tones. Four studies noted no effect of user skin tone on accuracy. The remaining two studies showed mixed results.

CONCLUSIONS

Preliminary evidence is inconclusive, but some studies suggest that wearable devices may be less accurate for detecting heart rate in participants with darker skin tones. Higher quality evidence is necessary, with larger sample sizes and more objective stratification of participants by skin tone, in order to characterize potential racial bias in consumer devices.

Validity and Efficacy of the Elite HRV Smartphone Application during Slow-Paced Breathing

Slow-paced breathing is a clinical intervention used to increase heart rate variability (HRV). The practice is made more accessible via cost-free smartphone applications like Elite HRV. We investigated whether Elite HRV can accurately measure and augment HRV via its slow-paced breathing feature. Twenty young adults completed one counterbalanced cross-over protocol involving 10 min each of supine spontaneous (SPONT) and paced (PACED; 6 breaths·min-1) breathing while RR intervals were simultaneously recorded via a Polar H10 paired with Elite HRV and reference electrocardiography (ECG). Individual differences in HRV between devices were predominately skewed, reflecting a tendency for Elite HRV to underestimate ECG-derived values. Skewness was typically driven by a limited number of outliers as median bias values were ≤1.3 ms and relative agreement was ≥very large for time-domain parameters. Despite no significant bias and ≥large relative agreement for frequency-domain parameters, limits of agreement (LOAs) were excessively wide and tended to be wider during PACED for all HRV parameters. PACED significantly increased low-frequency power (LF) for Elite HRV and ECG, and between-condition differences showed very large relative agreement. Elite HRV-guided slow-paced breathing effectively increased LF values, but it demonstrated greater precision during SPONT and in computing time-domain HRV.

Blood pressure and heart rate variability responses following an acute bout of vinyasa yoga and a prolonged seated control: A randomized crossover trial

Vinyasa yoga is moderate-intensity physical activity, yet physiological responses are poorly characterized. The purpose of this study was to examine the effect of a vinyasa yoga session on autonomic/cardiovascular functioning in healthy adults. A randomized crossover design took place at the Physical Activity and Weight Management laboratory (Pittsburgh, PA; n = 18), and included two experimental conditions: 60 minutes of vinyasa yoga or a seated control, and measurements were taken at baseline, 5-minutes, and 65-minute post-conditions. The primary cardiovascular-related outcomes of this study included blood pressure (BP), heart rate (HR), and HR variability (HRV) measures [natural log transformed (ln) standard deviation of normal-to-normal R-R intervals (SDNN), root mean square of successive differences (RMSSD), high frequency (HF), and low frequency to high frequency ratio (LF/HF ratio)]. Linear mixed effects models were used for data analyses. Systolic BP was 8.14 mmHg lower at 5 minutes post yoga (p<0.001) but was not different 65 minutes post, compared to the control. HR was higher at 5- and 65-minutes post yoga compared to the control (10.49/4.70 bpm, respectively, both p<0.01). HRV was lower (worse) at 5 and 65 minutes post for lnSDNN, lnRMSSD, and lnHF (all p<0.01). LF/HF ratio was higher (worse) at 5 minutes post yoga compared to the control (difference = +0.38, p = 0.025), but not different at 65 minutes post between conditions. Compared to prolonged sitting, vinyasa yoga had variable effects on post-session autonomic function including favorable BP responses and unfavorable HR and HRV responses, further investigation is warranted.

Exploratory study of the acute and mid-term effects of using a novel dynamic meeting environment (Aeris®) on cognitive performance and neurophysiological responses

The purpose of the present study was to assess the acute and mid-term effects of the dynamic aeris®-meeting- environment on brain activity, cognitive performance, heart rate variability (HRV), sleepiness, mental workload (EEG-MWI), as well as local experienced discomfort (LED) in healthy adults. Twenty-four healthy adults (16 females, age: 25.2 ± 3.1 years old) were randomly assigned to either the control (i.e., conventional meeting environment, CG) or experimental (Aeris® dynamic meeting-environment, DG) group with a 1:1 allocation. Participants reported to the laboratory on two test sessions separated by a 2-week intervention period (5 meetings of 90 min each week). Spontaneous resting EEG and HRV activities, as well as attentional (D2-R test) and vigilance (PVT) cognitive performances, sleepiness perceptions, and EEG-MWI, were recorded at the beginning of each test session and immediately following the 90-min meeting. The LED was measured pre- and post-intervention. The changes (Δ) from pre- to post-90 min meeting and from pre- to post- intervention were computed to further examine the acute and mid-term effects, respectively. Compared to the CG, the DG showed higher Δ (pre-post 90 min-meeting) in fronto-central beta (z = -2.41, p = 0.016, d = 1.10) and gamma (z = -2.34, p = 0.019, d = 0.94) frequencies at post-intervention. From pre- to post-intervention, only the DG group showed a significant increase in fronto-central gamma response (Δ) to the meeting session (z = -2.09, p = 0.04, d = 1.08). The acute use of the Aeris®-meeting-environment during the 90-min meeting session seems to be supportive for (i) maintaining vigilance performance, as evidenced by the significant increase in N-lapses from pre- to post-90 min session only in the CG (p = 0.04, d = 0.99, Δ = 2.5 ± 3 lapses), and (ii) improving alertness, as evidenced by the lower sleepiness score (p = 0.05, d = -0.84) in DG compared to CG. The mid-term use of such an environment showed to blind the higher baseline values of EEG-MWI recorded in DG compared to CG (p = 0.01, d = 1.05) and may prevent lower-back discomfort (i.e., a significant increase only in CG with p = 0.05 and d = 0.78), suggesting a less mentally and physically exhausting meeting in this environment. There were no acute and/or mid-term effects of the dynamic meeting environment on any of the HRV parameters. These findings are of relevance in the field of neuroergonomics, as they give preliminary support to the advantages of meeting in a dynamic office compared to a static office environment.

From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability

More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep ("orthosomnia"). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., "light sleep"). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, κ = 0.79), as well as the H10 (84.4%, κ = 0.76), and VS (84.2%, κ = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.

Internal and external load during on-field training drills with an aim of improving the physical performance of players in professional soccer: a retrospective observational study

Extensive research has led to evidence-based methodological recommendations for appropriate prescription and implementation of different training drills to improve the physical performance of professional soccer players, while also pointing out limitations of drills. Less is known about the current methods used in an ecological context and the extent to which evidence-based considerations are applied. Knowledge of current practices might also enable to identify pitfalls in successful implementation and/or deficiencies in the communication of scientific knowledge. Thus, the aim of this study was to quantify and compare the load that players experience in an ecological context during drills that are frequently used, and in which there is an intention to improve the players' physical capacities. Therefore, a retrospective observational cohort study was conducted over a 14-month period, analyzing 9 load measures during training drills of 39 players of a team competing in the highest Swiss league. The load experienced by players was statistically significant different between the assessed drill categories for each load measure (all p < .001). This indicates different drills provide different stimuli. HIIT drills proved to be a more powerful tool of getting players to cover distances at high-speed and to spend time at an intensity ≥90% HRmax compared to sided games. The sprint distance of players was very low in all sided games and in most cases also in HIIT drills, in the latter the players also hardly performed any deceleration. In small goal-oriented sided games, players covered a greater distance per minute when outside floaters were present. Particularly regarding an improvement of the players' aerobic capacity, the present data emphasize the relevance for coaches to ensure an appropriate exposure. In general, the importance of individual load management in professional soccer is highlighted.

Is that My Heartbeat? Measuring and Understanding Modality-Dependent Cardiac Interoception in Virtual Reality

Measuring interoception ('perceiving internal bodily states') has diagnostic and wellbeing implications. Since heartbeats are distinct and frequent, various methods aim at measuring cardiac interoceptive accuracy (CIAcc). However, the role of exteroceptive modalities for representing heart rate (HR) across screen-based and Virtual Reality (VR) environments remains unclear. Using a PolarH10 HR monitor, we develop a modality-dependent cardiac recognition task that modifies displayed HR. In a mixed-factorial design (N=50), we investigate how task environment (Screen, VR), modality (Audio, Visual, Audio-Visual), and real-time HR modifications (±15%, ±30%, None) influence CIAcc, interoceptive awareness, mind-body measures, VR presence, and post-experience responses. Findings showed that participants confused their HR with underestimates up to 30%; environment did not affect CIAcc but influenced mind-related measures; modality did not influence CIAcc, however including audio increased interoceptive awareness; and VR presence inversely correlated with CIAcc. We contribute a lightweight and extensible cardiac interoception measurement method, and implications for biofeedback displays.

Predicting Office Workers' Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model's R2 of 0.48 and MAE of 16.62. The extended model's feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers.

Well-being as a performance pillar: a holistic approach for monitoring tennis players

This perspective article aims to discuss the usefulness of tools that can assist tennis professionals effectively manage the well-being of their players. This includes identifying and monitoring meaningful metrics (i.e., training load, training intensity, heart rate variability), as well as careful planning of training and competition schedules with appropriate recovery periods. The use of innovative training methods (i.e., repeated-sprint training in hypoxia and heat training), and proper dietary practices, along with biometric assessment for young players, represents should be considered. Adopting a holistic approach to decision-making about training and competition, balancing both health and performance considerations, is crucial for tennis players and their support teams. More research is needed to refine best practices for enhancing tennis performance while prioritizing the well-being of players.

Home-Based Dynamics of Sleepiness-Related Conditions Starting at Biological Evening and Later (Beyond Working)

Shift work requires round-the-clock readiness to perform professional duties, and the workers' performance highly depends on their sleepiness level, which can be underestimated during a shift. Various factors, including the time of day, can influence sleepiness in shift workers. The objective of this study was to explore the dynamics of sleepiness-related conditions assessed through heart rate variability analysis, starting from the biological evening and continuing in vivo (at home), without the need for artificial alertness support. The participants solely performed regular evening household duties. A total of 32 recordings were collected from the Subjective Sleepiness Dynamics Dataset for analysis. At 8:00 p.m. and every 30 min thereafter, the participants completed cyclic sleepiness scales (the KSS and the SSS) until the time they went to bed, while their heart rate was recorded. The results of the study indicated that during the biological evening, high sleepiness is associated with a 'stressed' condition characterized by higher sympathetic activation. Later on, it is associated with a 'drowsy' condition characterized by higher parasympathetic activation and a decline in heart rate variability. Our findings provide evidence that the type of condition experienced during high sleepiness depends on the biological time. This should be taken into account when managing work regimes in shift work and developing alertness detectors.

Touchless Heart Rate Monitoring from an Unmanned Aerial Vehicle Using Videoplethysmography

Motivation: The advancement of preventive medicine and, subsequently, telemedicine drives the need for noninvasive and remote measurements in patients' natural environments. Heart rate (HR) measurements are particularly promising and extensively researched due to their quick assessment and comprehensive representation of patients' conditions. However, in scenarios such as endurance training or emergencies, where HR measurement was not anticipated and direct access to victims is limited, no method enables obtaining HR results that are suitable even for triage. Methods: This paper presents the possibility of remotely measuring of human HR from a series of in-flight videos using videoplethysmography (VPG) along with skin detection, human pose estimation and image stabilization methods. An unmanned aerial vehicle (UAV) equipped with a camera captured ten segments of video footage featuring volunteers engaged in free walking and running activities in natural sunlight. The human pose was determined using the OpenPose algorithm, and subsequently, skin areas on the face and forearms were identified and tracked in consecutive frames. Ultimately, HR was estimated using several VPG methods: the green channel (G), green-red difference (GR), excess green (ExG), independent component analysis (ICA), and a plane orthogonal to the skin (POS). Results: When compared to simultaneous readings from a reference ECG-based wearable recorder, the root-mean-squared error ranged from 17.7 (G) to 27.7 (POS), with errors of less than 3.5 bpm achieved for the G and GR methods. Conclusions: These results demonstrate the acceptable accuracy of touchless human pulse measurement with the accompanying UAV-mounted camera. The method bridges the gap between HR-transmitting wearables and emergency HR recorders, and it has the potential to be advantageous in training or rescue scenarios in mountain, water, disaster, or battlefield settings.

An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions.

Peak Running, Mechanical, and Physiological Demands of Professional Men's Field Hockey Matches

The aim of this study was to investigate the peak running, mechanical, and physiological demands of players of different positions in professional men's field hockey matches. Eighteen professional male field hockey players participated in the study, and data were collected in eleven official matches. Players wore GPS units (Vector S7, Catapult Sports) and heart rate (HR) monitors (Polar H1, Polar Electros) to collect physical and physiological data. Physical and physiological output of forwards, midfielders, and defenders in full matches and during 1-min peak periods was analysed. For all metrics and positions, the values identified for the 1-min peak periods were greater than the average values of match play (p < 0.05). In terms of 1-min peak period Player Load, all three positions were significantly different from each other. Forwards achieved the highest Player Load per minute, while defenders the lowest. The distance per minute, high-speed distance per minute, and the relative average heart rate of defenders were significantly lower than of midfielders and forwards (p < 0.05). The current study revealed the peak running, mechanical, and physiological demands of professional men's field hockey matches. It is recommended when prescribing training programmes, to consider not only match average demands, but also peak demands. Forwards and midfielders displayed similar peak demands, while defenders had the lowest demands in all metrics except the number of accelerations and decelerations per minute. Player Load per minute can be used to identify the differences in peak mechanical demands between forwards and midfielders.

A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement.

METHODS

We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE).

RESULTS

ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG.

CONCLUSION

This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.

Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review

Wearable and portable devices capable of acquiring cardiac signals are at the frontier of the sport industry. They are becoming increasingly popular for monitoring physiological parameters while practicing sport, given the advances in miniaturized technologies, powerful data, and signal processing applications. Data and signals acquired by these devices are increasingly used to monitor athletes' performances and thus to define risk indices for sport-related cardiac diseases, such as sudden cardiac death. This scoping review investigated commercial wearable and portable devices employed for cardiac signal monitoring during sport activity. A systematic search of the literature was conducted on PubMed, Scopus, and Web of Science. After study selection, a total of 35 studies were included in the review. The studies were categorized based on the application of wearable or portable devices in (1) validation studies, (2) clinical studies, and (3) development studies. The analysis revealed that standardized protocols for validating these technologies are necessary. Indeed, results obtained from the validation studies turned out to be heterogeneous and scarcely comparable, since the metrological characteristics reported were different. Moreover, the validation of several devices was carried out during different sport activities. Finally, results from clinical studies highlighted that wearable devices are crucial to improve athletes' performance and to prevent adverse cardiovascular events.

The Virtual Sleep Lab-A Novel Method for Accurate Four-Class Sleep Staging Using Heart-Rate Variability from Low-Cost Wearables

Sleep staging based on polysomnography (PSG) performed by human experts is the de facto "gold standard" for the objective measurement of sleep. PSG and manual sleep staging is, however, personnel-intensive and time-consuming and it is thus impractical to monitor a person's sleep architecture over extended periods. Here, we present a novel, low-cost, automatized, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch four-class sleep staging approach (Wake, Light [N1 + N2], Deep, REM) based solely on inter-beat-interval (IBI) data. Having trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 full-night manually sleep-staged recordings, we tested the MCNN on sleep classification using the IBIs of two low-cost (<EUR 100) consumer wearables: an optical heart rate sensor (VS) and a breast belt (H10), both produced by POLAR®. The overall classification accuracy reached levels comparable to expert inter-rater reliability for both devices (VS: 81%, κ = 0.69; H10: 80.3%, κ = 0.69). In addition, we used the H10 and recorded daily ECG data from 49 participants with sleep complaints over the course of a digital CBT-I-based sleep training program implemented in the App NUKKUAA™. As proof of principle, we classified the IBIs extracted from H10 using the MCNN over the course of the training program and captured sleep-related changes. At the end of the program, participants reported significant improvements in subjective sleep quality and sleep onset latency. Similarly, objective sleep onset latency showed a trend toward improvement. Weekly sleep onset latency, wake time during sleep, and total sleep time also correlated significantly with the subjective reports. The combination of state-of-the-art machine learning with suitable wearables allows continuous and accurate monitoring of sleep in naturalistic settings with profound implications for answering basic and clinical research questions.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Comparison of Slate Safety Wearable Device to Ingestible Pill and Wearable Heart Rate Monitor

BACKGROUND

With the increase in concern for deaths and illness related to the increase in temperature globally, there is a growing need for real-time monitoring of workers for heat stress indicators. The purpose of this study was to determine the usability of the Slate Safety (SS) wearable physiological monitoring system.

METHODS

Twenty nurses performed a common task in a moderate or hot environment while wearing the SS device, the Polar 10 monitor, and having taken the e-Celsius ingestible pill. Data from each device was compared for correlation and accuracy.

RESULTS

High correlation was determined between the SS wearable device and the Polar 10 system (0.926) and the ingestible pill (0.595). The SS was comfortable to wear and easily monitored multiple participants from a distance.

CONCLUSIONS

The Slate Safety wearable device demonstrated accuracy in measuring core temperature and heart rate while not restricting the motion of the worker, and provided a remote monitoring platform for physiological parameters.