Chest Band Research Articles

Retracted] Effects of Attentional Withdrawal on Chinese Disabled Biathletes’ Shooting Performance

Under two situations of static empty gun preview and simulated competition, the validity of self‐suggestion language “preloading compaction,” which suggests attentional withdrawal, was verified, respectively. Methods. The experiments in the two parts were designed in a single factor group. The participants (Chinese disabled biathletes) fired 20 times (4 groups in total, 5 times in each group) both in pretest and posttest, and they used self‐suggestion before each group of firing in posttest. Optoelectronic shooting device based on wireless laser transceiver technology was selected to collect the shooting technical indicators, scores, and other information. The operation evaluation form reflected the subjective feelings of the participants, and the heart rate was measured by the chest band. Results. By the method of big data analysis, under the condition of static empty gun preview, the results showed that there was an extremely significant difference between shooting score and quality of triggering. All the marks on the operation evaluation form improved after the intervention. In the mock competition situation, shooting score has highly significant differences with quality of triggering and relative triggering value and significant difference with aiming ability. The marks of the operation evaluation form have been improved; the heart rate of firing all decreased in different degrees. Conclusions. The attentional withdrawal has positive effects on the shooting scores, technical indicators of optoelectronic shooting device, subjective feelings, and heart rate of disabled biathletes under the two conditions. TIRE (the timing of firing) is the most sensitive indicator and can be used as the focus of future training and scientific research.

Comparison between photoplethysmographic heart rate monitor from Polar Vantage M and Polar V800 with H10 chest strap while running on a treadmill: Validation of the Polar Precision PrimeTM photoplestimographic system

Chest bands have been the most used device to monitor heart rate during running. However, some runners feel uncomfortable with the use of bands due to the friction and pressure exerted on the chest. Thus, the aim of this study was to determine if the photoplethysmography (PPG) system Polar Precision Prime used in the Polar Vantage M watch could replace chest bands (Polar V800-H10) to monitor heart rate with the same precision. A group of 37 people, middle-distance and long-distance professional runners, participated in this study. The submaximal speed was determined using 50% of the participants’ maximum speed in the height of their season. The Polar Vantage M reported high correlation ( r > 0.84) and high ICC (ICC > 0.86) when comparing its heart rate monitor with the Polar V800 synchronised with H10 chest strap during recording intervals of more than 2 min. The systematic bias and random error were very small (<1 bpm), especially for the 600 s recording interval (0.26 ± 5.10 bpm). Nevertheless, the error increased for 10 s (−5.13 ± 9.20 bpm), 20 s (−8.65 ± 12.60 bpm) and 30 s (−10.71 ± 14.99 bpm) time intervals. In conclusion, the PPG Polar Precision Prime included in the Polar Vantage M demonstrates that it could be a valid alternative to chest bands for monitoring heart rate while running, taking into account some usage considerations, good strap adjustment and an initial calibration time during the first 2–3 min.

Abstract 292: Detection of Return of Spontaneous Circulation in Pseudo-Pulseless Electrical Activity Using Trans-thoracic and Trans-abdominal Bioimpedance

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a lifeless form of profound cardiac shock characterized by measurable cardiac mechanical activity without clinically detectable pulses. Patients in pseudo-PEA carry different prognoses than those in true PEA and may require different therapies. Noninvasive technologies capable of detecting return of spontaneous circulation (ROSC) are needed to guide transition therapy from ACLS to critical care. Electrical impedance (EI) measurements across the thorax and abdomen may reflect the state of the cardiovascular system and change with ROSC. Hypothesis: EI measured across the chest wall or abdomen can detect ROSC in p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. EI was measured with cutaneous belts around the chest and abdomen (Swisstom). p-PEA was induced by ventilation with 6% oxygen in 94% nitrogen and was defined as a systolic aortic pressure < 40 mmHg. Experiments in which ROSC occurred within 20 seconds of starting 100% FiO 2 were studied. EI waveforms were analyzed with fast fourier transforms, and the dominant frequencies of p-PEA and ROSC signals were subtracted to create a difference distribution. Results: With onset of ROSC, there was a characteristic frequency increase in EI measurable across both chest and abdominal bands. Fourier transform analysis demonstrated a significant difference in dominant frequencies between resuscitation and p-PEA. The median difference between resuscitation and p-PEA dominant frequencies were 5.49 in the thorax [95% CI 3.48 - 7.50] and 6.65 in the abdomen [95% CI 3.63 - 9.67]. Conclusions: There appear to be characteristic EI frequency patterns and changes in p-PEA and ROSC. If confirmed clinically, this indicates that EI may be a promising technology for non-invasive monitoring in cardiac arrest.

Development of an Elastic Piezoelectric Yarn for the Application of a Muscle Patch Sensor.

In this paper, an elastic poly(vinylidenefluoride-co-trifluoroethylene) piezoelectric yarn for the application of a muscle patch sensor is presented. The electrospinning method is used to fabricate the piezoelectric yarn, and different parameters were used to control the orientation and structure of piezoelectric fibers. We further develop a post-alignment process to reorganize the orientation of fibers and to reshape fiber microstructures. Two unique microstructures of piezoelectric fibers that have an excellent elastic performance were identified. This piezoelectric yarn is composed of skewed and crimped fibers that align along the elongation direction, and it can be cyclically stretched up to 65% strain with good linearity, durability, and repeatability. Its mechanical behavior is superior to randomly distributed and fully straightened piezoelectric fibers, and it is suitable for long-term use of larger strain sensing. Our study demonstrated that this piezoelectric yarn can be stretched for more than 12 h under a repeated 1 Hz cyclic deformation. Using this elastic piezoelectric yarn, a muscle patch sensor that can be attached to the skin over human muscles for real-time monitoring is developed. The concentric, eccentric, and isometric contractions of biceps and triceps can be measured simultaneously to study their contraction behaviors. To further verify whether this patch sensor can be used under intense exercise conditions, the contraction behavior of a soleus muscle during stationary jumping and running is monitored to demonstrate sensor performance. Finally, this patch sensor is sewed onto a chest band, and it is verified that both breathing movement and heartbeat can be monitored.

PLAYERLOADTM AND HEART RATE RESPONSE TO SMALL-SIDED GAMES SPECIALIZED TO ADDITIONAL FIELD PLAYER RULE IN HANDBALL

Aim. This study aimed to investigate PlayerLoad and heart rate responses of handball-based small-sided games which specifically designed with additional field player rule. Materials and Methods. Thirteen well-trained female handball players (2 goalkeepers, 2 pivots, 4 wings, and 5 backs) participated in this study. A total of eight Small-sided games (SSGs) on two different training days with separated at least 48-hour were conducted. SSGs executed with the inclusion of two each 5 vs 6 and 7 vs 6 handball specific games that mimic the numerical inferior or superior situations in game-play. PlayerLoad, acceleration, deceleration and change of direction were established by a wearable IMUs and heart rate monitored with a chest band. Results. There was no significant difference between PlayerLoadtotal, PlayerLoadmin-1, HRmax, HRavr, and HIE parameters during the SSGs that separated by two testing days. No significant differences were found in PlayerLoadtotal, PlayerLoadmin-1, HRmax, and HRavr, parameters attained from the main and opponent team. Results showed no significant differences between all 5 vs 6 and 7 vs 6 SSGs. There was a significantly lower PlayerLoadtotal in the goalkeepers than backs, wings and pivots. The mean and maximum heart rate of back players was significantly lower than wings and pivots. Conclusion. Current study set out the examiner that if tactical innovations in playing situations numerical inferiority or superiority situations 7vs6 and 5vs6, will make the difference in players’ physical efforts. Because of the raised usage of this tactical variations, recent studies only focused to technical variables of playing with no goalkeeper and additional field player. Therefore, it is crucial to obtain clear knowledge how to affect the players as physically. Hence, a definite need for revision of the training demands of the goalkeepers in handball.

Toward Early Severity Assessment of Obstructive Lung Disease Using Multi-Modal Wearable Sensor Data Fusion During Walking.

Early detection of chronic diseases helps to minimize the disease impact on patient's health and reduce the economic burden. Continuous monitoring of such diseases helps in the evaluation of rehabilitation program effectiveness as well as in the detection of exacerbation. The use of everyday wearables i.e. chest band, smartwatch and smart band equipped with good quality sensor and light weight machine learning algorithm for the early detection of diseases is very promising and holds tremendous potential as they are widely used. In this study, we have investigated the use of acceleration, electrocardiogram, and respiration sensor data from a chest band for the evaluation of obstructive lung disease severity. Recursive feature elimination technique has been used to identity top 15 features from a set of 62 features including gait characteristics, respiration pattern and heart rate variability. A precision of 0.93, recall of 0.91 and F-1 score of 0.92 have been achieved with a support vector machine for the classification of severe patients from the non-severe patients in a data set of 60 patients. In addition, the selected features showed significant correlation with the percentage of predicted FEV1.Clinical Relevance- The study result indicates that wearable sensor data collected during natural walk can be used in the early evaluation of pulmonary patients thus enabling them to seek medical attention and avoid exacerbation. In addition, it may serve as a complementary tool for pulmonary patient evaluation during a 6-minute walk test.

TagBreathe: Monitor Breathing with Commodity RFID Systems

Breath monitoring helps assess the general personal health and gives clues to chronic diseases. Yet, current breath monitoring technologies are inconvenient and intrusive. For instance, typical breath monitoring devices need to attach nasal probes or chest bands to users. Wireless sensing technologies have been applied to monitor breathing using radio waves without physical contact. Those wireless sensing technologies however require customized radios which are not readily available. More importantly, due to interference, such technologies do not work well with multiple users. When multiple users are present, the detection accuracy of existing systems decreases dramatically. In this paper, we propose to monitor users’ breathing using commercial-off-the-shelf (COTS) RFID systems. In our system, passive lightweight RFID tags are attached to users’ clothes and backscatter radio waves, and commodity RFID readers report low level data (e.g., phase values). We reliably detect the effective human respiration corresponded signal and track periodic body movement due to inhaling and exhaling by analyzing the low level data reported by commodity readers. To enhance the measurement robustness, we synthesize data streams from an array of multiple tags to improve the monitoring accuracy. Our design follows the standard EPC protocol which arbitrates collisions in the presence of multiple tags. We implement a prototype for the breath monitoring system with commodity RFID systems. The experiment results show that the prototype system can simultaneously monitor breathing with high accuracy even with the presence of multiple users.

A novel respiration pattern biometric prediction system based on artificial neural network

PurposeThe purpose of this paper is to introduce a novel respiration pattern-based biometric prediction system (BPS) by using artificial neural network (ANN).Design/methodology/approachRespiration patterns were obtained using a knitted piezoresistive smart chest band. The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data. Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers, ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model’s accuracy as well as to limit the risk of overfitting.FindingsFindings establish the feasibility of using respiratory patterns for biometric identification. Experimental results show that, with a learning rate drop factor = 0.5, the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent. Out of 51,338 test set, the model achieved 51,557 correctly classified instances and 169 misclassified instances.Practical implicationsThe findings provide an impetus for possible studies into the application of chest breathing sensors for human machine interfaces in the area of entertainment.Originality/valueThis is the first time respiratory patterns have been applied in biometric prediction system design.

A Multimodal Wearable System for Continuous and Real-Time Breathing Pattern Monitoring During Daily Activity.

This study aims to understand breathing patterns during daily activities by developing a wearable respiratory and activity monitoring (WRAM) system. A novel multimodal fusion architecture is proposed to calculate the respiratory and exercise parameters and simultaneously identify human actions. A hybrid hierarchical classification (HHC) algorithm combining deep learning and threshold-based methods is presented to distinguish 15 complex activities for accuracy enhancement and fast computation. A series of signal processing algorithms are utilized and integrated to calculate breathing and motion indices. The designed wireless communication structure achieves the interactions among chest bands, mobile devices, and the data processing center. The advantage of the proposed HHC method is evaluated by comparing the average accuracy (97.22%) and predictive time (0.0094s) with machine learning and deep learning approaches. The nine breathing patterns during 15 activities were analyzed by investigating the data from 12 subjects. With 12 hours of naturalistic data collected from one participant, the WRAM system reports the breathing and exercise performance within the identified motions. The demonstration shows the ability of the WRAM system to monitor multiple users breathing and exercise status in real-time. The present system demonstrates the usefulness of the framework of breathing pattern monitoring during daily activities, which may be potentially used in healthcare. The proposed multimodal based WRAM system offers new insights into the breathing function of exercise in action and presents a novel approach for precision medicine and health state monitoring.

Automated assessment of pulmonary patients using heart rate variability from everyday wearables

Abstract Everyday wearables with enhanced computational capacity, good quality sensors, and machine learning/artificial intelligence enabled algorithms have the potential to play a key role not only in the fitness and wellness sector but also in the field of disease diagnostics and monitoring. The major challenges are a limited number of sensors, reliable data collection and processing, and deployment of computationally efficient algorithms. In this multi-cohort study, we have used everyday wearables such as chest band and smartwatch to investigate the heart rate variability (HRV) of 131 subjects which include 40 healthy controls, 69 asthma patients, 9 COPD patients and 13 patients with a co-morbidity of asthma and COPD. We aimed at a comprehensive investigation by exploring a total of 58 features including time domain, frequency domain, non-linear and entropy measures of HRV to identify the HRV indices that provide significant discriminatory information for classification between healthy and pulmonary patients. Feature ranking has been done by the area under the receiver operating characteristics curve. Classification of patients with the 15 top ranked features using data from the chest band heart rate sensor as well as estimated HRV parameters from smartwatch PPG signal have been investigated separately. Using the chest band data, a classification accuracy of 82.07%, precision of 83.13%, recall of 81.53% and F-1 score of 81.7% have been achieved; whereas using the smartwatch data, a classification accuracy of 80%, precision of 79.9%, recall of 80% and F-1 score of 79.94% have been achieved for the test set with an AdaBoost classifier. Heart rate variability metrics also showed significant correlation with disease severity and impact on health-related quality of life as measured by pulmonary function test, Asthma Symptoms Utility Index and COPD assessment test score respectively. The results indicate that HRV analysis using everyday wearables may be helpful in the assessment and management of asthma and COPD.

Association between work in deforested, compared to forested, areas and human heat strain: an experimental study in a rural tropical environment

Background. With climate change, adverse human health effects caused by heat exposure are of increasing public health concern. Forests provide beneficial ecosystem services for human health, including local cooling. Few studies have assessed the relationship between deforestation and heat-related health effects in tropical, rural populations. We sought to determine whether deforested compared to forested landscapes are associated with increased physiological heat strain in a rural, tropical environment. Methods. We analyzed data from 363 healthy adult participants from ten villages who participated in a two-by-two factorial, randomized study in East Kalimantan, Indonesia from 10/1/17 to 11/6/17. Using simple randomization, field staff allocated participants equally to different conditions to conduct a 90 min outdoor activity, representative of typical work. Core body temperature (CBT) was estimated at each minute during the activity using a validated algorithm from baseline oral temperatures and sequential heart rate data, measured using chest band monitors. We used linear regression models, clustered by village and with a sandwich variance estimator, to assess the association between deforested versus forested conditions and the number of minutes each participant spent above an estimated CBT threshold of 38.5 °C. Results. Compared to those in the forested condition (n = 172), participants in the deforested condition (n = 159) spent an average of 3.08 (95% confidence interval (CI) 0.57, 5.60) additional minutes with an estimated CBT exceeding 38.5 °C, after adjustment for age, sex, body mass index, and experiment start time, with a larger difference among those who began the experiment after 12 noon (5.17 [95% CI 2.20, 8.15]). Conclusions. In this experimental study in a tropical, rural setting, activity in a deforested versus a forested setting was associated with increased objectively measured heat strain. Longer durations of hyperthermia can increase the risk of serious health outcomes. Land use decisions should consider the implications of deforestation on local heat exposure and health as well as on forest services, including carbon storage functions that impact climate change mitigation.

Perrogator: A Portable Energy-Efficient Interrogator for Dynamic Monitoring of Wavelength-Based Sensors in Wearable Applications.

In this paper, we report the development of a portable energy-efficient interrogator (Perrogator) for wavelength-based optical sensors. The interrogator is based on a compact solution encompassing a white light source and the spectral convolution between the sensor and a tunable filter, which is acquired by a photodetector, where a microcontroller has two functions: (i) To control the filter tuning and to (ii) acquire the photodetector signal. Then, the data is sent to a single-board computer for further signal processing. Furthermore, the employed single-board computer has a Wi-Fi module, which can be used to send the sensors data to the cloud. The proposed approach resulted in an interrogator with a resolution as high as 3.82 pm (for 15.64 nm sweeping range) and maximum acquisition frequency of about 210 Hz (with lower resolution ~15.30 pm). Perrogator was compared with a commercial fiber Bragg grating (FBG) interrogator for strain measurements and good agreement between both devices was found (1.226 pm/µε for the commercial interrogator and 1.201 pm/µε for the proposed approach with root mean square error of 0.0144 and 0.0153, respectively), where the Perrogator has the additional advantages of lower cost, higher portability and lower energy consumption. In order to demonstrate such advantages in conjunction with the high acquisition frequency allowed us to demonstrate two wearable applications using the proposed interrogation device over FBG and Fabry-Perot interferometer (FPI) sensors. In the first application, an FBG-embedded smart textile for knee angle assessment was used to analyze the gait of a healthy person. Due to the capability of reconstructing the FBG spectra, it was possible to employ a technique based on the FBG wavelength shift and reflectivity to decouple the effects of the bending angle and axial strain on the FBG response. The measurement of the knee angle as well as the estimation of the angular and axial displacements on the grating that can be correlated to the variations of the knee center of rotation were performed. In the second application, a FPI was embedded in a chest band for simultaneous measurement of breath and heart rates, where good agreement (error below 5%) was found with the reference sensors in all analyzed cases.

On-body Sensing of Cocaine Craving, Euphoria and Drug-Seeking Behavior Using Cardiac and Respiratory Signals

Drug addiction is a chronic brain-based disorder that affects a person's behavior and leads to an inability to control drug usage. Ubiquitous physiological sensing technologies to detect illicit drug use have been well studied and understood for different types of drugs. However, we currently lack the ability to continuously and passively measure the user state in ways that might shed light on the complex relationships between cocaine-induced subjective states (e.g., craving and euphoria) and compulsive drug-seeking behavior. More specifically, the applicability of wearable sensors to detect drug-related states is underexplored. In the current work, we take an initial step in the modeling of cocaine craving, euphoria and drug-seeking behavior using electrocardiographic (ECG) and respiratory signals unobtrusively collected from a wearable chest band. Ten experienced cocaine users were studied using a human laboratory paradigm of self-regulated (i.e., "binge") cocaine administration, during which self-reported visual analog scale (VAS) ratings of cocaine-induced subjective effects (i.e., craving and euphoria) and behavioral measures of drug-seeking behavior (i.e., button clicks for drug infusions) are collected. Our results are encouraging and show that self-reported VAS Craving scores are predicted with a normalized root-mean-squared error (NRMSE) of 17.6% and a Pearson correlation coefficient of 0.49. Similarly, for VAS Euphoria prediction, an NRMSE of 16.7% and a Pearson correlation coefficient of 0.73 were achieved. We further analyze the relative importance of different morphology-related ECG and respiratory features for craving and euphoria prediction. Demographic factor analysis reveals how one single factor (i.e., average dollar ($) per cocaine use) can help to further boost the performance of our craving and euphoria models. Lastly, we model drug-seeking behavior using cardiac and respiratory signals. Specifically, we demonstrate that the latter signals can predict participant button clicks with an F1 score of 0.80 and estimate different levels of click density with a correlation coefficient of 0.85 and an NRMSE of 17.9%.

WearBreathing

Respiratory rate is a vital physiological signal that may be useful for a multitude of clinical applications, especially if measured in the wild rather than controlled settings. In-the-wild respiratory rate monitoring is currently done using dedicated chest band sensors, but these devices are specialized, expensive and cumbersome to wear day after day. While recent works have proposed using smartwatch based accelerometer and gyroscope data for respiratory rate monitoring, current methods are unreliable and inaccurate in the presence of motion and have therefore only been applied in controlled or low-motion settings. Thus, measuring respiratory rate in the wild remains a challenge. We observe that for many applications, having fewer accurate readings is better than having more, less accurate readings. Based on this, we develop WearBreathing, a novel system for respiratory rate monitoring. WearBreathing consists of a machine learning based filter that detects and rejects sensor data that are not suitable for respiratory rate extraction and a convolutional neural network model for extracting respiratory rate from accelerometer and gyroscope data. Using a diverse, out-of-the-lab dataset that we collected, we show that WearBreathing has a 2.5 to 5.8 times lower mean absolute error (MAE) than existing approaches. We show that WearBreathing is tunable and by changing a single threshold value, it can, for example, deliver a reading every 50 seconds with a MAE of 2.05 breaths/min or a reading every 5 minutes with an MAE of 1.09 breaths/min. Finally, we evaluate power consumption and find that with some power saving measures, WearBreathing can run on a smartwatch while providing a full day's worth of battery life.

Dual channel electrodermal activity and an ECG wearable sensor to measure mental stress from the hands

<p class="Abstract"><span lang="EN-US">The paper describes the design and characterisation of a dual-channel electrodermal activity (EDA) and ECG sensor for acquiring data from the hands. The need for dual-channel data acquisition is due to the removal of motion artefacts that may happen when EDA is measured on subjects when they are moving their hands in their everyday activities. The ECG channel is measured from the hands using the same electrodes that have already been used for EDA. This choice reduces the invasiveness of ECG measurement with respect to the usual vests or chest bands. The characterisation demonstrates high-level performance of the sensor in terms of linearity and jitter, even if the measurement on the hands provides a weaker ECG signal with respect to chest derivations. Even when the subject is using their hands, no artefacts were found in extracting the heart rate from ECG. </span></p>

Knitted piezoresistive smart chest band and its application for respiration patterns assessment

Recognizing alterations in respiration patterns is an essential early sign not only for disease diagnoses but also for determining psychosomatic condition of the individual. The desire for measuring these parameters outside the hospital setting has led to a great interest in embedding this functionality in everyday wearable materials. This study presents an assessment of respiration patterns using a knitted piezoresistive smart chest band. A total of 20 users—Asians and Africans from 8 countries—were tested 14 times over a 67-day period. Experiments were conducted with users being under several varied test conditions including standing and sitting posture, varying physical activities, and different times in the day. Results show a successful application of the band with result patterns being in congruence with studies conducted using other related respiratory measurement technologies and instruments. Sedentary standing posture presented less signal noise compared to physical activity tests. Individuality and the repeatability of results over time suggest feasibility of the use of breath patterns as a biometric feature. Influences of ethnicity, gender, and body mass index on respiration patterns have also been discussed.

The evolution of the physical effort curve during the physical education and sports lesson

Abstract Introduction: In physical and sports education, the effort dosing is essential for achieving the goals and is managed by altering the effort parameters and correlating these with a certain type of rest, respecting the physical norms of the body. According to a study, analyzing and comparison of “The Gauss curve” during a physical education and sport lesson, the expected rising of the effort curve was denied during its first 3 stages. Especially between stage 2 (Preparation of the body for effort) and stage 3 (Selective influence of the locomotive apparatus) there is a decrease of the heart rate. During the sports lessons, we recorded for 10 hours the heart rate of 10 children from 2 different classes of the same grade, 5-A and 5-B, creating one control group (of 5 boys) and one experimental group (5 boys). For the experimental group we recorded for 10 different classes, interchanging stages 2 with 3, more exactly, the stage “Selective influence of the locomotive apparatus” was done before the stage “Preparation of the body for effort”, and for the control group we also recorded for 10 different classes, but they performed the normal sports lesson, following the stages in chronological order. Objective of the study: The objective of this research was to discover/identify if the effort curve follows an ascendant trajectory during the first 3 stages and a descendant curve during the last 3, by analyzing the heart rate for each stage during the physical education and sports lesson. Material and methods: The data was collected using 5 - Polar M400 watches and 5 - H7 chest bands, during sport lessons inside the School “Aghiresu Fabricii” from Cluj-Napoca. After the recorded data of the heart rates were analyzed, we observed the differences between the classic sport lesson and the one with the stages 2 and 3 reversed, for the 5th grade subjects. Results: By comparing the data obtained from the recordings, it was found that there are some differences between the classical physical education lesson and that in which interventions were made in the second and third stages, as a result of the change in heart rate values in the investigated subjects. Conclusion: Analyzing the two groups in which we had different approaches in the physical education and sports lesson, it is denied that in the classical lesson we have a curve of ascending effort in the first stages. As a result of the reversal of the two-to-three stages, an ascending curve – the Gaussian Curve – is achieved through which an optimal adaptation to effort is made in the fundamental part of the physical education and sports lesson.

Quality of an ambulatory monitoring technique for diagnosing obstructive sleep apnea under conditions of limited resources.

Objectives:To: 1) evaluate the quality of an ambulatory monitoring technique for diagnosing Obstructive Sleep Apnea Syndrome (OSAS) while patients move through the city; and 2) identify factors that lead to data loss.Methods:Clinical histories were reviewed and ambulatory portable monitorings of adults with high pretest probability for OSAS were included, the signals monitored were pulse oximetry, heart rate, nasal pressure, snoring, chest band and body position. The equipment was connected from 14:00-20:00 h and then patients moved through the city turning it off and on at home. Results were analyzed visually to record all the minutes lost. A good-quality study was defined as recording time 240 min and signal loss <20%. A cost/benefit analysis was performed using Golpe et al.'s methodology.Results:A total of 70 recordings were analyzed. Most subjects were obese men with severe OSAS. Signal quality was determined to be good with a median signal loss of 4.9 min (0-405) that represented 1% (0-99) of total recording time. The signal lost most often was pulse oximetry at 1.8 min (0-403, p=0.0001). Of the 70 studies performed, 57 (81%) met the definition of good quality, while 13 (19%) had to be repeated. Men lost the pulse oximetry signal more often than women. This technique could represent savings of 65-75%.Conclusions:Placing a portable OSAS monitor during the day while patients move around the city turning it on and off at home does not affect the quality of the study results obtained and is a cost-effective method.

Wavelet Phase Coherence Analysis between the Respiratory Activity and the Microcirculation—The Effects of Type 1 Diabetes

The microvascular perfusion is modulated by physiological functions, which have been associated with specific frequency ranges. Although cardio-respiratory interactions have been widely analyzed at a macrocirculation level, only few studies have been conducted with the aim to understand the effect of breathing on the peripheral circulation. Diabetic microangiopathy is characterized by structural and functional alterations of the blood microvessels, which reflect in a modification of the physical properties of peripheral microvascular beds, such as vascular stiffness. A wavelet phase coherence (WPC) analysis was performed to elucidate the time-phase relationship between the respiratory activity and vascular-dependent features of the peripheral pulse, reconstructed by means of a novel multi-Gaussian modeling algorithm. The present WPC analysis was conducted on a group of 24 healthy volunteers, mean age 30±15 years, in comparison with 21 type-1 diabetic (T1D) subjects, mean age 26±3 years, in order to detect the effects of T1D on the physiological coupling between breathing and microcirculation. The peripheral cutaneous perfusion was assessed on the pulp of the index finger with the laser Doppler flowmetry (LDF) technique, while the respiratory signal was simultaneously recorded with a wearable chest band. In 17 out of the 24 control subjects involved (i.e., 71%), significant WPC values were identified in the respiratory frequency range (0.145-0.6 Hz), with respect to the area beneath the diastolic portion of the peripheral pulse (median WPC: 77.9%). This outcome indicates that the diastolic phase of the pulse exhibits a marked periodicity linked to the breathing activity. The analysis conducted on the T1D group highlighted a loss of synchronization, with significant WPC levels found in only 33% of the investigated subjects. These results suggest that the proposed method is able to detect the detrimental effects of T1D on the blood microcirculation. Disclosure A. Perrella: None. M. Sorelli: None. P. Francia: None. L. Capirchio: None. R. Anichini: Advisory Panel; Self; Abbott. Speaker's Bureau; Self; Novartis Pharmaceuticals Corporation, Boehringer Ingelheim GmbH. Advisory Panel; Self; Eli Lilly and Company, AstraZeneca. Speaker's Bureau; Self; Merck &amp; Co., Inc., Takeda Development Centre Europe Ltd.. Advisory Panel; Self; Sanofi, Janssen Pharmaceuticals, Inc.. Research Support; Self; Grünenthal Group. A. de Bellis: Speaker's Bureau; Self; Eli Lilly and Company, AstraZeneca, Novo Nordisk A/S. Research Support; Self; Grünenthal Group. M. Gulisano: None. L. Bocchi: None.

Influence of the chest compression measurement method on assessment of restraint performance in side-impact crash scenarios

Side impact crashes contribute a significant number of fatal injuries (25% of road fatalities in the USA in 2016), with severe thoracic injuries diagnosed in 58% of front near-side impact occupants. Epidemiological data indicate that thoracic-only side airbags (tSABs) are not as effective as laboratory testing has suggested, and one of the reasons for this may be the use of surrogate-specific injury assessment methods, which are not directly transferable between Anthropometric Test Devices (ATDs) and Post-Mortem Human Surrogates (PMHSs). This study examines the effect of the thorax deformation measurement location and method on the predicted performance of seatbelts and tSABs in a side impact using a Human Body Model (HBM). The HBM was integrated in a vehicle and subjected to a Moving Deformable Barrier (MDB) impact at 61 km/h, with four restraint configurations: belted and unbelted, with and without a tSAB. Occupant response was assessed through chest band (CB) deformation, and as a change in distance between markers on the ribs. Multiple measurement locations in the HBM enabled direct comparison between the methods. The CB method indicated a 35% increase of chest compression due to tSAB; the rib-deflection (RD) method was not sensitive to the tSAB. The RD method predicted a 20% reduction of chest compression due to the seatbelt, but the CB-measured change was negligible. This study highlights the importance of measurement method on the response outcome and demonstrates that different outcomes may be predicted using a HBM for the same impact scenario, depending on the measurement method.