Energy expenditure estimation from respiration variables

A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation

Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model's parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.

A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices

We present a multi-device and multi-modal dataset, called WEEE, collected from 17 participants while they were performing different physical activities. WEEE contains: (1) sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist); (2) respiratory data collected with an indirect calorimeter serving as ground-truth information; (3) demographics and body composition data (e.g., fat percentage); (4) intensity level and type of physical activities, along with their corresponding metabolic equivalent of task (MET) values; and (5) answers to questionnaires about participants' physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations, we believe that the dataset will enable the development of novel human energy expenditure (EE) estimation techniques for a diverse set of application scenarios. EE refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. A reliable estimate of people's EE thus enables computing systems to make inferences about users' physical activity and help them promoting a healthier lifestyle.

Validation of ambulatory monitoring devices to measure energy expenditure and heart rate in a military setting

Objectives.To investigate the validity of different devices and algorithms used in military organizations worldwide to assess physical activity energy expenditure (PAEE) and heart rate (HR) among soldiers.Design.Device validation study.Methods. Twenty-three male participants serving their mandatory military service accomplished, firstly, nine different military specific activities indoors, and secondly, a normal military routine outdoors. Participants wore simultaneously an ActiHeart, Everion, MetaMax 3B, Garmin Fenix 3, Hidalgo EQ02, and PADIS 2.0 system. The PAEE and HR data of each system were compared to the criterion measures MetaMax 3B and Hidalgo EQ02, respectively.Results. Overall, the recorded systematic errors in PAEE estimation ranged from 0.1 (±1.8) kcal.min^-1to -1.7 (±1.8) kcal.min^-1for the systems PADIS 2.0 and Hidalgo EQ02 running the Royal Dutch Army algorithm, respectively, and in the HR assessment ranged from -0.1 (±2.1) b.min^-1to 0.8 (±3.0) b.min^-1for the PADIS 2.0 and ActiHeart systems, respectively. The mean absolute percentage error (MAPE) in PAEE estimation ranged from 29.9% to 75.1%, with only the Everion system showing an overall MAPE <30%, but all investigated devices reported overall MAPE <1.4% in the HR assessment.Conclusions. The present study demonstrated poor to moderate validity in terms of PAEE estimation, but excellent validity in all investigated devices in terms of HR assessment. Overall, the Everion performed among the best in both parameters and with a device placement on the upper arm, the Everion system is particularly useful during military service, as it does not interfere with other relevant equipment.

COVID-19 and physical activity in sedentary individuals: differences in metabolic, cardiovascular, and respiratory responses during aerobic exercise performed with and without a surgical face masks

BACKGROUND

The Coronavirus-19 (COVID-19) impairs metabolic, cardiovascular, and pulmonary functions in human metabolism, and wearing face masks is recommended for the prevention of contracting or exposing others to cardio-respiratory infections. Since the effect of wearing a surgical face mask (SFM) on cardiopulmonary exercise capacity has not been systematically reported we aimed to determine the effects of wearing SFM during an incremental walking test on metabolic, cardiovascular, and pulmonary gas exchange responses in sedentary individuals.

METHODS

The evaluations were performed using a repeated measures study design. Seven sedentary males (age:40years, height:178cm, weight:88kg, BMI:28kg/m2, VO2max:32.7±3.9ml/kg/min) and 7 sedentary female participants (age:34years, height:169cm, weight:62kg, BMI:22kg/m2, VO2max:32.1±6.8 ml/kg/min) volunteered to participate in the current study. Anthropometric parameters were measured using a Bioelectrical impedance analysis prior to each testing session. The measures of lung function assessed by spirometry, breathing pattern, maximal exercise capacity with-and-without mask were measured with a breath-by-breath automated exercise metabolic system during incremental Bruce protocol on a treadmill with two consecutive sessions with 48-h intervals. Blood pressure values (systolic and diastolic pressure) of the individuals were taken and recorded within 1 minute at the end of every ten minutes, without speed changes.

RESULTS

VO2, VCO2, and VE were significantly lower during exercise performed with SFM (p<0.001). Heart rate, systolic and diastolic blood pressure were also found significantly higher during exercise performed with SFM (p<0.01).

CONCLUSIONS

Wearing a SFM during incremental walking predispose a decrease in oxygen delivery while increasing pulmonary ventilation in sedentary individuals. Thus, it could be speculated that surgical face masks have a negative impact on oxygen delivery during exercise which results in decreased exercise performance due to the restricted ventilatory conditions.

Leptin alters energy intake and fat mass but not energy expenditure in lean subjects

Based on studies in mice, leptin was expected to decrease body weight in obese individuals. However, the majority of the obese are hyperleptinemic and do not respond to leptin treatment, suggesting the presence of leptin tolerance and questioning the role of leptin as regulator of energy balance in humans. We thus performed detailed novel measurements and analyses of samples and data from our clinical trials biobank to investigate leptin effects on mechanisms of weight regulation in lean normo- and mildly hypo-leptinemic individuals without genetic disorders. We demonstrate that short-term leptin administration alters food intake during refeeding after fasting, whereas long-term leptin treatment reduces fat mass and body weight, and transiently alters circulating free fatty acids in lean mildly hypoleptinemic individuals. Leptin levels before treatment initiation and leptin dose do not predict the observed weight loss in lean individuals suggesting a saturable effect of leptin. In contrast to data from animal studies, leptin treatment does not affect energy expenditure, lipid utilization, SNS activity, heart rate, blood pressure or lean body mass.

Leptin treatment is effective to reduce body weight in animal models, but patients with obesity and associated hyperleptinemia do not respond well to leptin therapy. Here the authors report a retrospective analysis of four clinical trials in normo- and mildly hypoleptinemic individuals and show that leptin therapy alters food intake in the short term and reduces weight and fat mass in the long term without effects on energy expenditure.

Three-dimensional analysis of sexual dimorphism in the soft tissue morphology of the upper airways in a human population

OBJECTIVES

Several studies have analyzed the sexual dimorphism of the skeletal cranial airways. This study aimed to quantify the three-dimensional (3D) morphology of the soft tissues of the upper airways in a human population. We addressed hypotheses about morphological features related to respiratory and energetic aspects of nasal sexual dimorphism.

METHODS

We reconstructed 3D models of 41 male and female soft tissue nasal airways from computed tomography data. We measured 280 landmarks and semilandmarks for 3D-geometric morphometric analyses to test for differences in size and 3D morphology of different functional compartments of the soft tissue airways.

RESULTS

We found statistical evidence for sexual dimorphism: Males were larger than females. 3D features indicated taller and wider inflow tracts, taller outflow tracts and slightly taller internal airways in males. These characteristics are compatible with greater airflow in males.

DISCUSSION

The differences in 3D nasal airway morphology are compatible with the respiratory-energetics hypothesis according to which males differ from females because of greater energetic demands. Accordingly, structures related to inflow and outflow of air show stronger signals than structures relevant for air-conditioning.

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO₂), which is commonly assessed by heart rate (HR) in field studies. However, the VO₂-HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of -3.94 and 2.00 mL/min/kg, while the HR-Flex model had -5.01 and 5.36 mL/min/kg and the branched model, -6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation. Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.

Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21⁻65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R² = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R² = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R² = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.

Ribcage measurements indicate greater lung capacity in Neanderthals and Lower Pleistocene hominins compared to modern humans

Our most recent fossil relatives, the Neanderthals, had a large brain and a very heavy body compared to modern humans. This type of body requires high levels of energetic intake. While food (meat and fat consumption) is a source of energy, oxygen via respiration is also necessary for metabolism. We would therefore expect Neanderthals to have large respiratory capacities. Here we estimate the pulmonary capacities of Neanderthals, based on costal measurements and physiological data from a modern human comparative sample. The Kebara 2 male had a lung volume of about 9.04 l; Tabun C1, a female individual, a lung volume of 5.85 l; and a Neanderthal from the El Sidrón site, a lung volume of 9.03 l. These volumes are approximately 20% greater than the corresponding volumes of modern humans of the same body size and sex. These results show that the Neanderthal body was highly sensitive to energy supply.

Daniel García-Martínez et al. report Neanderthal lung volume estimates based on measurements from rib bone fossils and lung capacity data from modern humans. They estimate that Neanderthal individuals had approximately 20% higher lung capacity than modern humans, possibly due to higher energy requirements.