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Study Objectives: Unobtrusive monitoring of sleep and sleep disorders in children presents challenges. We investigated the possibility of using Ultra-Wide band (UWB) radar to measure sleep in children.Methods: Thirty-two children scheduled to undergo a clinical polysomnography participated; their ages ranged from 2 months to 14 years. During the polysomnography, the children's body movements and breathing rate were measured by an UWB-radar. A total of 38 features were calculated from the motion signals and breathing rate obtained from the raw radar signals. Adaptive boosting was used as machine learning classifier to estimate sleep stages, with polysomnography as gold standard method for comparison.Results: Data of all participants combined, this study achieved a Cohen's Kappa coefficient of 0.67 and an overall accuracy of 89.8% for wake and sleep classification, a Kappa of 0.47 and an accuracy of 72.9% for wake, rapid-eye-movement (REM) sleep, and non-REM sleep classification, and a Kappa of 0.43 and an accuracy of 58.0% for wake, REM sleep, light sleep and deep sleep classification.Conclusion: Although the current performance is not sufficient for clinical use yet, UWB radar is a promising method for non-contact sleep analysis in children. [ABSTRACT FROM AUTHOR]

Breathing rates are often collected both in the wild and in captivity to inform on cetaceans' internal state. However, few studies have investigated the effect of various factors on this breathing rate. We investigated the variations of individual and synchronous breathing rates depending on individual features (species, sex, age), displayed behavior, social parameters (social grouping), and environmental parameters (diurnal variation, presence of enrichment, unusual events, and presence of visitors in three groups of captive odontocetes (Yangtze finless porpoises, Neophocaena asiaeorentalis asiaeorientalis, East‐Asian finless porpoises, Neophocaena asiaeorentalis sunameri, bottlenose dolphins, Tursiops truncatus). Both individual and synchronous breathing rates were the highest when animals engaged in energetic or social behaviors. Individual breathing rate decreased but synchronous breathing rate increased with the presence of enrichment. Both rates increased during unusual events (e.g., pool cleaning, presence of a diver in the pool, noise, transport) and when public was present for Yangtze finless porpoises. Finally, synchronous breathing rate increased for Yangtze finless porpoises when experiencing social separation. We suggest that individual and synchronous breathing rates are useful parameters to measure, both in wild and captive animals, to obtain information on their arousal/stress state. However, these rates should be interpreted with caution and should be used together with other parameters to allow more accurate inferences. [ABSTRACT FROM AUTHOR]

Assessments of respiratory response and animal activity are useful endpoints in drug pharmacology and safety research. We investigated whether continuous, direct monitoring of breathing rate and body motion in animals in the home cage using the Vum Digital Smart House can complement standard measurements in enabling more granular detection of the onset and severity of physiologic events related to lung injury in a well-established rodent model of paraquat (PQ) toxicity. In rats administered PQ, breathing rate was significantly elevated while body motion was significantly reduced following dosing and extending throughout the 14-day study duration for breathing rate and at least 5 days for both nighttime and daytime body motion. Time course differences in these endpoints in response to the potential ameliorative test article bardoxolone were also readily detected. More complete than standard in-life measurements, breathing rate and body motion tracked injury progression continuously over the full study time period and aligned with, and informed on interval changes in clinical pathology. In addition, breathing rates correlated with terminal pathology measurements, such as normalized lung weights and histologic alveolar damage and edema. This study is a preliminary evaluation of the technology; our results demonstrate that continuously measured breathing rate and body motion served as physiologically relevant readouts to assess lung injury progression and drug response in a respiratory injury animal model.

Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson’s correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device.

The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.

In this study, a thermal imaging instrument was used to obtain facial thermal image information which was then used to calculate the number of breaths taken. However, small movements were inevitable and the first issue addressed was the means by which image calibration and region selection was to be made. To this end, thermal image sequence data calibration was done using technology that resolved small natural deviations in the nostril area. After these problems had been solved, a Hampel filter was used to process the nostril area signals. The independent component method was used to filter the effects of non‐respiratory signals, and the least squares method was employed for smoothing. Savitzky‐Golay filtering was used to adjust the signal baseline and the processed nostril region thermal image signals were compared with standard abdominal breathing band signals. Results showed that the difference in the number of breaths per minute was less than 1.5 times. The usual normal respiratory frequency range lies within a range of 0.1–0.5Hz. In the calculation of 'coherence obtained from MVAR model', the spectral coherence analysis results showed the methods proposed in this study can substantially enhance the relevance between 0.15 and 0.2Hz. [ABSTRACT FROM AUTHOR]

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Assessments of respiratory response and animal activity are useful endpoints in drug pharmacology and safety research. We investigated whether continuous, direct monitoring of breathing rate and body motion in animals in the home cage using the Vum Digital Smart House can complement standard measurements in enabling more granular detection of the onset and severity of physiologic events related to lung injury in a well-established rodent model of paraquat (PQ) toxicity. In rats administered PQ, breathing rate was significantly elevated while body motion was significantly reduced following dosing and extending throughout the 14-day study duration for breathing rate and at least 5 days for both nighttime and daytime body motion. Time course differences in these endpoints in response to the potential ameliorative test article bardoxolone were also readily detected. More complete than standard in-life measurements, breathing rate and body motion tracked injury progression continuously over the full study time period and aligned with, and informed on interval changes in clinical pathology. In addition, breathing rates correlated with terminal pathology measurements, such as normalized lung weights and histologic alveolar damage and edema. This study is a preliminary evaluation of the technology; our results demonstrate that continuously measured breathing rate and body motion served as physiologically relevant readouts to assess lung injury progression and drug response in a respiratory injury animal model. [ABSTRACT FROM AUTHOR]

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car. [ABSTRACT FROM AUTHOR]

Recently, noninvasive vital signs detection is widely used in through the wall (TTW) human detection and healthcare monitoring. In indoors and TTW environments, low signal to noise ratio make the detection of the weak vital signs difficult, and causes errors in the estimation of heartbeat, respiration rate. In this paper, different techniques are applied to the signal collected using a M-sequence ultra wideband sensor from the human subject, in order to suppress the clutter, detect the target range and estimate the heartbeat and respiration rate. First, moving average method is used to remove the stationary clutter. Next, Singular Value Decomposition (SVD) is applied to cancel the non-stationary clutter. Then, variance analysis is applied to estimate the range; when range has been detected, one dimensional Singular Spectrum Analysis (SSA) is applied to the range bin containing the subject data. It is observed that SSA completely succeeds to decompose the signal into heartbeat and respiration signal and a collection of unwanted signals as clutter, noise, and harmonics. Finally, Fast Fourier transform (FFT) is used for the estimation of the heartbeat and respiration rate. Comparison results show that the proposed algorithm outperforms a reference SVD based algorithms for heartbeat and respiration rate estimation. [ABSTRACT FROM AUTHOR]

Objective: The objective of this paper is to obtain accurate estimation of breathing rate (BR), using only the electrocardiogram (ECG) or the photoplethysmogram (PPG) signals, to avoid wearing cumbersome and uncomfortable sensors for direct measurements. Methods: Several respiration waveforms are derived from ECG or PPG signals based on amplitude, frequency, and baseline wander modulations. It is, however, difficult to determine their optimal combination for BR estimation due to the noise and patient specificity. We first propose to quantify the quality of modulation waveforms using respiratory quality indices (RQIs). We then present two methods: the first automatically selects the modulation signal with highest RQI for BR estimation, and the second tracks the respiration signal using the Kalman smoother to fuse modulation signals with highest RQI. Results: These two methods are evaluated on two independent datasets, one benchmark database (DB) with immobilized patients recordings and the second with those performing daily activities. Our results outperform existing methods in the literature in both the cases. Conclusion: Experimental results show that the RQIs coupled with a fusion algorithm increases the accuracy for BR estimations in dealing with derived modulation signals. Significance: This work describes a robust Kalman Smoother method applicable in multiple clinical contexts to improve breathing rate estimation from data fusion. [ABSTRACT FROM AUTHOR]

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.

The aim of this paper is designing an algorithm dubbed "pattern" to detect electrocardiogram (ECG) components accurately by searching exactly in the right places of peaks and getting exhaustive information related to the heart. Then, using the obtained results to propose a method for constructing respiration signal properly, by calculating the mean of R peaks to determine inspiration and expiration phases and calculating the amount of change for other peaks to be added during inspiration phase and subtracted during the expiration phase. The proposed method improves envelope method which only depends on the size of R to construct respiration signals. The results show that the pattern algorithm is guaranteed method and useful for detecting ECG components and exploiting them for constructing respiration signal work better than envelope method.

Research works on operator monitoring underline the benefit of taking into consideration several signal modalities to improve accuracy for an objective mental state diagnosis. Heart rate (HR) is one of the most utilized systemic measures to assess cognitive workload (CW), whereas, respiration parameters are hardly utilized. This study aims at verifying the contribution of analyzing respiratory signals to extract features to evaluate driver’s activity and CW variations in driving. Eighteen subjects participated in the study. The participants carried out two different cognitive tasks requiring different CW demands, a single task as well as a competing cognitive task realized while driving in a simulator. Our results confirm that both HR and breathing rate (BR) increase in driving and are sensitive to CW. However, HR and BR are differently modulated by the CW variations in driving. Specifically, HR is affected by both driving activity and CW, whereas, BR is suitable to evidence a variation of CW only when driving is not required. On the other hand, spectral features characterizing respiratory signal could be also used similarly to HR variability indices to detect high CW episodes. These results hint the use of respiration as an alternative to HR to monitor the driver mental state in autonomic vehicles in order to predict the available cognitive resources if the user has to take over the vehicle.

Measuring a breathing rate has multiple clinical applications from early detection of diseases to monitoring of patients in a critical condition. Several techniques have been introduced to measure the breathing rate. The conventional breathing rate monitoring systems are contact-based and expensive, and sometimes their presence will interfere with the normal breathing system and thus produce an incorrect reading. Furthermore, they are not used in case a patient skin is sensitive (or burned skin). This paper presents a contact-free breathing rate measurement using video analysis. To accomplish this, we introduced two algorithms. The first one is approximating chest regions from face detection algorithm, and the second one is using peak-to-peak measurement to calculate a breathing rate of a person on the incoming signal. We tested the proposed algorithm on 10 subjects, and the result shows an overall 11.61% increase in accuracy (with 84.66% average accuracy) compared with FFT-based implementation. Furthermore, the time complexity of the proposed breathing rate measurement method is O(logn), whereas FFT implementation is O(nlogn). Thus, the proposed system is promising that it can be used as a standalone system or as a component of another application. [ABSTRACT FROM AUTHOR]

Breathing rate (BR) is a significant bio marker that provides both prognostic and diagnostic information for monitoring physiological condition. In addition to vital bio markers, such as blood oxygen saturation and pulse rate, BR can be extracted from non-invasive and wearable pulse oximeter based photoplethysmogram (PPG). Empirical mode decomposition (EMD) and its noise-assisted variants are widely used for decomposing non-linear and non-stationary signals. In this work, the effect of all variants of EMD in extracting BR from PPG has been investigated. We have used an EMD family PCA based hybrid model in extracting BR from PPG, which is a natural extension of our previously developed ensemble EMD (EEMD) PCA hybrid model. The performance of each model has been tested using two different datasets: MIMIC and Capnobase. Median absolute error varied from 0 to 5.03 and from 2.47 to 10.55 breaths/min for MIMIC and Capnobase dataset, respectively. Among all the EMD variants, EEMD-PCA and improved complete EEMD with adaptive noise (ICEEMDAN) PCA hybrid model present better performance for both datasets. This is the first study to compare EMD variants performance for decomposing real world signal and determine that from current methods, ICEEMDAN and EEMD are optimal for estimating BR from PPG. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the vital signals. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise, are wanted to be separated clearly. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. To solve this problem, a novel method is provided, in this paper, to extract the heartbeat and the breath information simultaneously. The method uses the feature time index with the first valley peak of the energy function of intrinsic mode functions (FVPIEF) calculated by pseudo bi-dimension ensemble empirical mode decomposition method and extracts the vital signals by the ensemble empirical mode decomposition (EEMD). Both simulation and experiment results evidently show that the proposed FVPIEF based two-layer EEMD method is effective for separating the small heartbeat signal from the large breath signal and significantly improves the evaluation of heart and breathing rates in both hold-breathing and breathing conditions. [ABSTRACT FROM AUTHOR]

Wireless body sensors are increasingly used by clinicians and researchers in a wide range of applications, such as sports, space engineering, and medicine. Monitoring vital signs in real time can dramatically increase diagnosis accuracy and enable automatic curing procedures, e.g., detect and stop epilepsy or narcolepsy seizures. Breathing parameters are critical in oxygen therapy, hospital, and ambulatory monitoring, while the assessment of cough severity is essential when dealing with several diseases, such as chronic obstructive pulmonary disease. In this paper, a low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing. This system uses wearable wireless multimodal patch sensors, designed using off-the-shelf components. These wearable sensors use a low-power nine-axis inertial measurement unit to quantify the respiratory movement and a MEMs microphone to record audio signals. Data processing and fusion algorithms are used to calculate the respiratory frequency and the coughing events. The architecture of each wireless patch-sensor is presented. In fact, the results show that the small $26.67 \times 65.53$ mm2 patch-sensor consumes around 12–16.2 mA and can last at least 6 h with a miniature 100-mA lithium ion battery. The data processing algorithms, the acquisition, and wireless communication units are described. The proposed network performance is presented for experimental tests with a freely behaving user in parallel with the gold standard respiratory inductance plethysmography. [ABSTRACT FROM AUTHOR]

Highlights • POF-embedded smart textile for breath and heart rates monitoring. • Analytical approach for the sensor operation principle. • Technique for detecting heart and breath rates even when the subject is moving. • Signal processing to mitigate the influence of gait frequency in the sensor response. Abstract In this paper, we present the development of a polymer optical fiber (POF) sensor for simultaneous measurement of breath (BR) and heart rates (HR). The sensor is embedded as a smart textile solution that can be used within the user’s clothes. In addition, a signal processing technique is proposed for obtaining the HR and BR without the influence of body movements and in different positions of the user’s chest. Sensor signal processing and analysis are made in the frequency domain and different filters are applied. Results show errors below 4 beats per minute and 2 breaths per minute for the HR and BR, respectively, even when the user is performing periodic body movements such as the ones induced by the gait. Thus, the proposed POF-based smart textile is a low-cost solution with good accuracy that can be readily applied in the remote monitoring of patients at home without disturbing their daily activities. [ABSTRACT FROM AUTHOR]

Continued developments of sensor technology including hardware miniaturization and increased sensitivity have enabled the development of less intrusive methods to monitor physiological parameters during daily life. In this work, we present methods to recover cardiac and respiratory parameters using accelerometer and gyroscope sensors on the wrist. We demonstrate accurate measurements in a controlled laboratory study where participants (n = 12) held three different positions (standing up, sitting down and lying down) under relaxed and aroused conditions. In particular, we show it is possible to achieve a mean absolute error of 1.27 beats per minute (STD: 3.37) for heart rate and 0.38 breaths per minute (STD: 1.19) for breathing rate when comparing performance with FDA-cleared sensors. Furthermore, we show comparable performance with a state-of-the-art wrist-worn heart rate monitor, and when monitoring heart rate of three individuals during two consecutive nights of in-situ sleep measurements.

Since predominant high temperatures could alter thermic equilibrium, the knowledge of dairy cattle thermoregulation characteristics on tropical environment is very important. The higher energy consume to maintain homeothermy could damage production and reproduction. Therefore, this revision aimed to analyze some thermoregulatory characteristics and fur of dairy cattle housed in tropical environment. The maximum and minimum values of rectal temperature found was 41.14°C (½ Gir ½ Holstein) and 37.09°C (lactating cows in the morning), breathing rate of 137.12mov/min (½ Gir ½ Holstein) and 22mov.min-1 (Holstein and Girolanda), sweating rate of 239.42g.m-2 .h-1 (½ Gir ½ Holstein) and 90.5g.m-2 .h-1 (¾ Holstein Zebu), surface temperature of 44.0°C (Holstein at 12 hours) and 22.0°C (½ Gir ½ Holstein, at 6am), hair coat thickness of 5.31mm ( ½ Gir ½ Holstein) and 1.93mm (Holstein), length of hair coat of 16.11mm (Holstein) and 8.08mm (Caracu Caldeano). The rectal temperature and breathing rate of dairy cattle raised on tropical environment could be altered by intrinsic and extrinsic factors. The hair coat thickness and length are lower on summer and spring compared with fall and winter.

We develop and evaluate a respiratory rate (RR) estimation algorithm that utilizes data from the pressure-sensitive mat (PSM) technology for continuous patient monitoring in neonatal intensive care units. An analysis of the random effect of drift and systematic effect of creep in the PSM data is presented, showing that these are essentially dependent on the applied load and contact surface. Uncertainty measurements are pivotal when estimating physiologic parameters. The standard uncertainty in the PSM data is here represented by the percent drift. Next, we evaluate the applicability of the PSM technology to estimate RR in neonatal patient simulator trials under five mixed effects including internally and externally induced motion, mattress type, grunting, laying position, and different breathing rates. We analyze the limits of agreement on the mixed effects model to derive the uncertainty in the estimated RR obtained through two estimation techniques. In comparison with the gold standard RR values, we achieved a mean bias of 0.56 breaths per minute (bpm) with an error bounded by a 95% confidence interval of [−2.26, 3.37] bpm. These results meet the clinical accuracy requirements of RR within ±5 bpm. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnoea is one of the most common sleep disorders, which is characterized by the repetitive cessations of respiratory flow during sleep. This paper describes an alternative method to detect sleep parameters by measuring a variation in the earth’s magnetic field. A magnetometer sensor, laid onto the body, detects millimeter night-time breathing movements by measuring the change in the magnetic vectors. A soft, non-invasive wearable sensor is designed to be printed on a miniature printed circuit board, packaged in a minimal invasive board, which includes a wireless Bluetooth low-energy (BLE) module and a low-power microcontroller. A very low power consumption is achieved by applying a smart processing algorithm, which is also described. The algorithm delivers respiration rate, apnoea time, and time in movement. A comparison with a well-known air-flow sensor is also discussed. [ABSTRACT FROM PUBLISHER]

Nocturnal vocal activity in dolphins is often thought to be associated with feeding activity. However, when no food resources are available dolphins spend their time for the most part resting/sleeping. While unihemispherically sleeping, dolphins mostly swim slowly and synchronously in close proximity with one or more other individuals. Although vocal activity is lower during resting/sleeping, dolphins are not entirely silent the entire night. However, nothing is known about the temporal patterning of vocal activity at night and its potential relation with activity in dolphins. Here we recorded the vocal activity of a group of five captive bottlenose dolphins at night while having no feeding opportunity, examined whether there was any temporal pattern and/or a relation with breathing activity, used here as an index of overall activity. The temporal pattern revealed two peaks of intense whistle activity (8 p.m. and midnight), which were followed by a strong decrease of whistle rate and a slight decrease of respiration rate. We suggest that the high vocal activity at the peak periods might indicate socializing periods and that dolphins, like many other species, show periods of increased social and vocal interactions (chorusing?) before starting to rest/sleep, maybe to ensure the synchrony of slow swimming observed in this species. These findings contribute to a better understanding of nocturnal vocal activity in cetaceans and suggest new lines of research on vocal/social activity of dolphins in relation to presleep and resting behavior.

In this work, a fiber Bragg grating (FBG) based sensing system for wheelchair pressure ulcer prevention was developed. Six FBGs were strategically positioned in a wheelchair to monitor the more prominent bone areas, namely scapulas (right (SR) and left (SL)), ischiatic zone (right (IR) and left (IL)), and heels (right (HR) and left (HL)). The sensing architecture was tested by a female user during pressure relief exercises, to verify its effectiveness on pressure monitoring. The proposed system proves to be a compact and reliable solution for wheelchair pressure ulcer prevention, making it a suitable alternative to existing conventional electronic sensors, with the advantage of being immune to electromagnetic interferences and usable in humid environments. In addition to the pressure, the breathing rate was also monitored. By combining the proposed sensing architecture with a wheelchair user detection software, it is possible to create alerts for the user to know when a new position should be adopted, in order to relieve the pressure in a specific area, thus avoiding one of the biggest problems for such patients, pressure ulcers.

This study is based on the relationship between meditation, the present moment, and psychophysiology. We employed the metronome task to operationalize the extension of the present moment. A pre-post longitudinal study was conducted. The performance in the metronome task was compared before and after the interventions (meditation, story). The aim was to assess whether physiological changes (heart, breathing) during meditation influence the temporal-integration (TI) of metronome beats. Mindfulness meditators either meditated (n = 41) or listened to a story (n = 43). The heart and breathing activity were recorded during the intervention and compared to a resting-state condition. By applying path analyses we found that meditation led to an increase of the duration of integration intervals at the slowest metronome frequency (inter-stimulus interval, ISI = 3 s). After meditation, the higher the heart-rate variability (i.e., the root mean square of successive differences, RMSSD), the longer the duration of integration intervals at the fastest frequency (ISI = 0.33 s). Moreover, the higher the breathing rate during meditation, the greater the integration of intervals at ISI = 1 s. These findings add evidence to meditation-induced changes on the TI of metronome beats and the concept of the embodiment of mental functioning.

The study focuses on psychological and physical effects of stress while performing community garden activities of various intensity levels. The aim of this study was to determine the psychological and physical effects in adults with (case group) and without (control group) mental disabilities. Salivary α-amylase (sAA) levels and the stress response scale (SRS-18) were used for the psychological analysis (n = 42). For physical assessment (n = 13), electrocardiogram (ECG), surface electromyogram (sEMG), and respiration rate were continuously measured while performing the activities using a multichannel telemetry system. The results showed that following the activities, the case group exhibited decreasing sAA levels while control group exhibited increasing sAA levels. However, both groups exhibited lower SRS-18 results following the activities. Compared with the control group, the case group had a significantly lower increase in the ratio of the heart rate (IRHR) (5.5%) during low-intensity work (filling pots with soil), but a significantly higher IRHR (16.7%) during high-intensity work (turning over soil). The case group experienced significantly higher levels of fatigue during high-intensity work (digging) than during the rest condition. These findings indicate that appropriate workload allocation, according to health, is necessary in the community garden setting because reducing the intensity of work assignments for people with mental disabilities will reduce their physical stress. [ABSTRACT FROM AUTHOR]

Personality traits, the consistent individual behavioral differences, are currently gaining much attention in studies of natural bird populations. However, associations between personality traits and parasite infections are not often investigated. Even less attention has been given to studies of birds in the high-elevation region such as the Tibetan plateau. This research aims to examine the relationship between avian malaria parasites and two personality traits in a population of the Chestnut Thrush ( Turdus rubrocanus ) breed in the Tibetan plateau. Our results revealed no evidence of sex bias in malaria parasite prevalence. Furthermore, we found no effect of infection status on two personality scores: activity and boldness. Additionally, no effects on the activity level or boldness were observed for different parasite lineages of Haemoproteus , Leucocytozoon , the sex of the birds, or their interactions. Similarly, we did not find any relationship between activity level and boldness with nestling numbers, sex, or their interactions. Notably, individuals with a larger number of offspring tended to display greater boldness. Our findings indicate that blood parasite infections are common in this population but do not significantly impact the personality of the birds., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)