Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
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ABSTRACT:
SUBMITTER: Amelard R
PROVIDER: S-EPMC8586225 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted during ADL was strongly correlated (r?=?0.87, P?<?0.001) with the measured and the prediction bias was 0.2?ml·min-1·kg-1. The MNG calculated based on predicted was strongly correlated (r?=?0.71, P?<?0.001) with MNG calculated based on measured data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.
| S-EPMC5381118 | biostudies-literature
Project description:BACKGROUND:Over the past several decades, naturally occurring and man-made mass casualty incidents (MCIs) have increased in frequency and number worldwide. To test the impact of such events on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardized method to collect and analyze data from mass casualty exercises is needed to assess preparedness and performance of the health care staff involved. OBJECTIVE:In this study, we aimed to assess the feasibility of using wearable proximity sensors to measure proximity events during an MCI simulation. In the first instance, our objective was to demonstrate how proximity sensors can collect spatial and temporal information about the interactions between medical staff and patients during an MCI exercise in a quasi-autonomous way. In addition, we assessed how the deployment of this technology could help improve future simulations by analyzing the flow of patients in the hospital. METHODS:Data were obtained and collected through the deployment of wearable proximity sensors during an MCI functional exercise. The scenario included 2 areas: the accident site and the Advanced Medical Post, and the exercise lasted 3 hours. A total of 238 participants were involved in the exercise and classified in categories according to their role: 14 medical doctors, 16 nurses, 134 victims, 47 Emergency Medical Services staff members, and 27 health care assistants and other hospital support staff. Each victim was assigned a score related to the severity of his/her injury. Each participant wore a proximity sensor, and in addition, 30 fixed devices were placed in the field hospital. RESULTS:The contact networks show a heterogeneous distribution of the cumulative time spent in proximity by the participants. We obtained contact matrices based on the cumulative time spent in proximity between the victims and rescuers. Our results showed that the time spent in proximity by the health care teams with the victims is related to the severity of the patient's injury. The analysis of patients' flow showed that the presence of patients in the rooms of the hospital is consistent with the triage code and diagnosis, and no obvious bottlenecks were found. CONCLUSIONS:Our study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during an MCI simulation. It represents, to our knowledge, the first example of unsupervised data collection-ie, without the need for the involvement of observers, which could compromise the realism of the exercise-of face-to-face contacts during an MCI exercise. Moreover, by permitting detailed data collection about the simulation, such as data related to the flow of patients in the hospital, such deployment provides highly relevant input for the improvement of MCI resource allocation and management.
| S-EPMC6658323 | biostudies-literature
Project description:Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.
| S-EPMC6928687 | biostudies-literature
Project description:BackgroundSubmaximal exercise testing can have a greater application in clinical settings because peak exercise testing is generally not available. In previous work, a prediction equation was developed to estimate peak oxygen consumption (Vo2) using a total body recumbent stepper (TBRS) and the Young Men's Christian Association (YMCA) protocol in adults who were healthy.ObjectiveThe purpose of the present study was to cross-validate the TBRS peak Vo2 prediction equation in older adults.DesignA cross-sectional study was conducted.MethodsThirty participants (22 female, 8 male; mean age=66.8 years, SD=5.52; mean weight=68.51 kg, SD=13.39) who previously completed a peak exercise test and met the inclusion criteria were invited to participate in the cross-validation study. Within 5 days of the peak Vo2 test, participants completed the TBRS submaximal exercise test. The TBRS submaximal exercise test equation was used to estimate peak Vo2. The variables in the equation included age, weight, sex, watts (at the end of the submaximal exercise test), and heart rate (at the end of the submaximal exercise test).ResultsA strong correlation was found between the predicted peak Vo2 and the measured peak Vo2. The difference between the values was 0.9 mL·kg(-1)·min(-1), which was not statistically different. The standard error of the estimate was 4.2 mL·kg(-1)·min(-1).LimitationsThe sample included individuals who volunteered to perform a peak exercise test, which may have biased the results toward those willing to exercise to fatigue.ConclusionThe data suggest the TBRS submaximal exercise test and prediction equation can be used to predict peak Vo2 in older adults. This finding is important for health care professionals wanting to provide information to their patients or clients regarding their fitness level.
| S-EPMC4016679 | biostudies-literature
Project description:BackgroundTo estimate oxygen uptake (VO2) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch. CPX is an important risk stratification tool for patients with heart failure (HF) owing to the prognostic value of the features derived from the gas exchange variables such as VO2. However, CPX requires specialized equipment, as well as trained professionals to conduct the study.Methods and resultsWe have conducted a total of 68 CPX tests on 59 patients with HF with reduced ejection fraction (31% women, mean age 55 ± 13 years, ejection fraction 0.27 ± 0.11, 79% stage C). The patients were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing set (n = 44) and a separate validation set (n = 24). We developed globalized (population) regression models to estimate VO2 from the SCG and ECG signals measured continuously with the patch. We further classified the patients as stage D or C using the SCG and ECG features to assess the ability to detect clinical state from the wearable patch measurements alone. We developed the regression and classification model with cross-validation on the training-testing set and validated the models on the validation set. The regression model to estimate VO2 from the wearable features yielded a moderate correlation (R2 of 0.64) with a root mean square error of 2.51 ± 1.12 mL · kg-1 · min-1 on the training-testing set, whereas R2 and root mean square error on the validation set were 0.76 and 2.28 ± 0.93 mL · kg-1 · min-1, respectively. Furthermore, the classification of clinical state yielded accuracy, sensitivity, specificity, and an area under the receiver operating characteristic curve values of 0.84, 0.91, 0.64, and 0.74, respectively, for the training-testing set, and 0.83, 0.86, 0.67, and 0.92, respectively, for the validation set.ConclusionsWearable SCG and ECG can assess CPX VO2 and thereby classify clinical status for patients with HF. These methods may provide value in the risk stratification of patients with HF by tracking cardiopulmonary parameters and clinical status outside of specialized settings, potentially allowing for more frequent assessments to be performed during longitudinal monitoring and treatment.
| S-EPMC7704799 | biostudies-literature
Project description:PurposeWe tested the hypothesis that the described increase in oxygen uptake ([Formula: see text])-plateau incidence following a heavy-severe prior exercise is caused by a steeper increase in [Formula: see text] and muscle fiber activation in the submaximal intensity domain.MethodsTwenty-one male participants performed a standard ramp test, a [Formula: see text] verification bout, an unprimed ramp test with an individualized ramp slope and a primed ramp test with the same ramp slope, which was preceded by an intensive exercise at 50% of the difference between gas exchange threshold and maximum workload. Muscle fiber activation was recorded from vastus lateralis, vastus medialis, and gastrocnemius medialis using a surface electromyography (EMG) device in a subgroup of 11 participants. Linear regression analyses were used to calculate the [Formula: see text]-([Formula: see text]) and EMG-(?RMS/?P) ramp test kinetics.ResultsTwenty out of the 21 participants confirmed their [Formula: see text] in the verification bout. The [Formula: see text]-plateau incidence in these participants did not differ between the unprimed (n?=?8) and primed (n?=?7) ramp test (p?=?0.500). The [Formula: see text] was lower in the primed compared to the unprimed ramp test (9.40?±?0.66 vs. 10.31?±?0.67 ml min-1 W-1, p?<?0.001), whereas the ?RMS/?P did not differ between the ramp tests (0.62?±?0.15 vs. 0.66?±?0.14% W-1; p?=?0.744).ConclusionThese findings do not support previous studies, which reported an increase in [Formula: see text]-plateau incidence as well as steeper increases in [Formula: see text] and muscle fiber activation in the submaximal intensity domain following a heavy-severe prior exercise.
| S-EPMC7419380 | biostudies-literature
Project description:Syncope is a common disorder with a lifetime prevalence of about 40%. Implantable cardiac electronic devices, including implantable loop recorders (ILR) and implantable cardioverter-defibrillators (ICD), are well established in syncope management. However, despite the successful use of ILR and ICD, diagnosis and therapy still remain challenging in many patients due to the complex hemodynamic interplay of cardiac and vascular adaptations during impending syncopes. Wearable sensors might overcome some limitations, including misdiagnosis and inappropriate defibrillator shocks, because a variety of physiological measures can now be easily acquired by a single non-invasive device at high signal quality. In neurally-mediated syncope (NMS), which is the most common cause of syncope, advanced signal processing methodologies paved the way to develop devices for early syncope detection. In contrast to the relatively benign NMS, in arrhythmia-related syncopes immediate therapeutical intervention, predominantly by electrical defibrillation, is often mandatory. However, in patients with a transient risk of arrhythmia-related syncope, limitations of ICD therapy might outweigh their potential therapeutic benefits. In this context the wearable cardioverter-defibrillator offers alternative therapeutical options for some high-risk patients. Herein, we review recent evidence demonstrating that wearable sensors might be useful to overcome some limitations of implantable devices in syncope management.
| S-EPMC4315629 | biostudies-literature
Project description:In this work we introduce a new series of ratiometric oxygen sensors based on phosphorescent cyclometalated iridium centers partnered with organic coumarin fluorophores. Three different cyclometalating ligands and two different pyridyl-containing coumarin types were used to prepare six target complexes with tunable excited-state energies. Three of the complexes display dual emission, with fluorescence arising from the coumarin ligand, and phosphorescence from either the cyclometalated iridium center or the coumarin. These dual-emitting complexes function as ratiometric oxygen sensors, with the phosphorescence quenched under O2 while fluorescence is unaffected. The use of blue-fluorescent coumarins results in good signal resolution between fluorescence and phosphorescence. Moreover, the sensitivity and dynamic range, measured with Stern-Volmer analysis, can be tuned two orders of magnitude by virtue of our ability to synthetically control the triplet excited-state ordering. The complex with cyclometalated iridium 3MLCT phosphorescence operates under hyperoxic conditions, whereas the two complexes with coumarin-centered phosphorescence are sensitive to very low levels of O2 and function as hypoxic sensors.
| S-EPMC9350586 | biostudies-literature
Project description:Thiophene moieties were incorporated into previously described Zinspy (ZS) fluorescent Zn(II) sensor motifs (Nolan, E. M.; Lippard, S. J. Inorg. Chem. 2004, 43, 8310-8317) to provide enhanced fluorescence properties, low-micromolar dissociation constants for Zn(II), and improved Zn(II) selectivity. Halogenation of the xanthenone and benzoate moieties of the fluorescein platform systematically modulates the excitation and emission profiles, pH-dependent fluorescence, Zn(II) affinity, and Zn(II) complexation rates, offering a general strategy for tuning multiple properties of xanthenone-based metal ion sensors. Extensive biological studies in cultured cells and primary neuronal cultures demonstrate 2-{6-hydroxy-3-oxo-4,5-bis[(pyridin-2-ylmethylthiophen-2-ylmethylamino)methyl]-3H-xanthen-9-yl}benzoic acid (ZS5) to be a versatile imaging tool for detecting Zn(II) in vivo. ZS5 localizes to the mitochondria of HeLa cells and allows visualization of glutamate-mediated Zn(II) uptake in dendrites and Zn(II) release resulting from nitrosative stress in neurons.
| S-EPMC2002492 | biostudies-literature
Project description:Automated insulin delivery is the new standard for type 1 diabetes, but exercise-related hypoglycemia remains a challenge. Our aim was to determine whether a dual-hormone closed-loop system using wearable sensors to detect exercise and adjust dosing to reduce exercise-related hypoglycemia would outperform other forms of closed-loop and open-loop therapy.Participants underwent four arms in randomized order: dual-hormone, single-hormone, predictive low glucose suspend, and continuation of current care over 4 outpatient days. Each arm included three moderate-intensity aerobic exercise sessions. The two primary outcomes were percentage of time in hypoglycemia (<70 mg/dL) and in a target range (70-180 mg/dL) assessed across the entire study and from the start of the in-clinic exercise until the next meal.The analysis included 20 adults with type 1 diabetes who completed all arms. The mean time (SD) in hypoglycemia was the lowest with dual-hormone during the exercise period: 3.4% (4.5) vs. 8.3% (12.6) single-hormone (P = 0.009) vs. 7.6% (8.0) predictive low glucose suspend (P < 0.001) vs. 4.3% (6.8) current care where pre-exercise insulin adjustments were allowed (P = 0.49). Time in hypoglycemia was also the lowest with dual-hormone during the entire 4-day study: 1.3% (1.0) vs. 2.8% (1.7) single-hormone (P < 0.001) vs. 2.0% (1.5) predictive low glucose suspend (P = 0.04) vs. 3.1% (3.2) current care (P = 0.007). Time in range during the entire study was the highest with single-hormone: 74.3% (8.0) vs. 72.0% (10.8) dual-hormone (P = 0.44).The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.
| S-EPMC6014543 | biostudies-literature