Assessment of energy expenditure during high intensity cycling and running using a heart rate and activity monitor in young active adults

Highly Sensitive Biomimetic Crack Pressure Sensor with Selective Frequency Response

High-sensitivity sensors in practical applications face the issue of environmental noise interference, requiring additional noise reduction circuits or filtering algorithms to improve the signal-to-noise ratio (SNR). To address this issue, this study proposes a biomimetic crack pressure sensor with selective frequency response based on hydrogel dampers. The core of this research is to construct a biomimetic crack pressure sensor with selective frequency response using the high-pass filtering characteristics of gelatin-chitosan hydrogels. This design, inspired by the slit sensilla and stratum corneum structure of spider legs, delves into the material properties and principles of hydrogel dampers, exploring their application in biomimetic crack pressure sensors, including parameter selection, structural design, and performance optimization. By delving into the nuanced characteristics and working principles of hydrogel dampers, their integration in biomimetic crack pressure sensors is examined, focusing on aspects like parameter selection, structural engineering, and performance enhancement to selectively sieve out low-frequency noise and transmit target vibrational signals. Experimental results demonstrate that this innovative sensor, while suppressing low-frequency vibration signals, can selectively detect high-frequency signals with high sensitivity. At different vibration frequencies, the relative change in resistance exceeds 200%, and the sensor sensitivity is 7 × 104 kPa-1. Furthermore, this sensor was applied to human voice detection, and the corresponding results verified its frequency-selective performance evidently. This study not only proposes a new design for pressure sensors but also offers fresh insights into the application of biomimetic crack pressure sensors in intricate environments.

Energy Expenditure, Intensity, and Perceived Effort in Recreational Functional Training

Background: Functional training (FT) has become popular and seems to provoke health benefits. However, there are unsubstantiated claims regarding energy expenditure (EE) vs. weight-loss and cardiorespiratory improvements linked to FT. Objective: This study quantified the EE and intensity during FT performed in a conventional fitness center. Additionally, data of FT and moderate continuous walking (WLK) were compared. Methods: Healthy individuals with no previous experience with FT [n = 25, 11 males/14 females, 38.8 ± 9.3 years; 73.9 ± 13.8 Kg; 168.5 ± 8.5 cm; 26.0 ± 4.5 Kg/m2; 16 overweight (BMI >25 Kg/m2)] performed three FT sessions interspersed with 48 h (two familiarization, one assessment). The circuit included 4 rounds of 12 exercises performed at all-out intensity for 20 s with 1-min intervals between rounds. WLK was performed for 25 min with intensity corresponding to scores 3-5 on Borg CR-10 Scale. Outcomes were EE (kcal), movement counts estimated by triaxial accelerometry, heart rate reserve (%HRR), and rate of perceived exertion (RPE). Results: On average, FT sessions lasted 24 min and EE ranged between 124 and 292 kcal (188 ± 41 kcal), corresponding to 5-8 METs (6.1 ± 0.6 METs), and 70-80%HRR (74 ± 8%). Accelerometry (counts/min) showed that vigorous predominated over moderate intensity during FT and WLK (p = .01), with similar EE. The relative intensity and RPE were higher in FT vs. WLK (74% vs. 55%HRR and Borg 5-8 vs. 3-5, respectively; p < .0001). Conclusion: FT and WLK elicited EE consistent with recommendations to reduce cardiovascular disease risk, but only FT achieved relative intensities compatible with cardiorespiratory improvement. FT should be considered an option in health-oriented exercise programs for the general population.

Estimating Metabolic Energy Expenditure During Level Running in Healthy, Military-Age Women and Men

ABSTRACT

Looney, DP, Hoogkamer, W, Kram, R, Arellano, CJ, and Spiering, BA. Estimating metabolic energy expenditure during level running in healthy, military-age women and men. J Strength Cond Res 37(12): 2496-2503, 2023-Quantifying the rate of metabolic energy expenditure (Ṁ) of varied aerobic exercise modalities is important for optimizing fueling and performance and maintaining safety in military personnel operating in extreme conditions. However, although equations exist for estimating oxygen uptake during running, surprisingly, there are no general equations that estimate Ṁ. Our purpose was to generate a general equation for estimating Ṁ during level running in healthy, military-age (18-44 years) women and men. We compiled indirect calorimetry data collected during treadmill running from 3 types of sources: original individual subject data (n = 45), published individual subject data (30 studies; n = 421), and published group mean data (20 studies, n = 619). Linear and quadratic equations were fit on the aggregated data set using a mixed-effects modeling approach. A chi-squared (χ2) difference test was conducted to determine whether the more complex quadratic equation was justified (p < 0.05). Our primary indicator of model goodness-of-fit was the root-mean-square deviation (RMSD). We also examined whether individual characteristics (age, height, body mass, and maximal oxygen uptake [V̇O2max]) could minimize prediction errors. The compiled data set exhibited considerable variability in Ṁ (14.54 ± 3.52 W·kg-1), respiratory exchange ratios (0.89 ± 0.06), and running speeds (3.50 ± 0.86 m·s-1). The quadratic regression equation had reduced residual sum of squares compared with the linear fit (χ2, 3,484; p < 0.001), with higher combined accuracy and precision (RMSD, 1.31 vs. 1.33 W·kg-1). Age (p = 0.034), height (p = 0.026), and body mass (p = 0.019) were associated with the magnitude of under and overestimation, which was not the case for V̇O2max (p = 0.898). The newly derived running energy expenditure estimation (RE3) model accurately predicts level running Ṁ at speeds from 1.78 to 5.70 m·s-1 in healthy, military-age women and men. Users can rely on the following equations for improved predictions of running Ṁ as a function of running speed (S, m·s-1) in either watts (W·kg-1 = 4.43 + 1.51·S + 0.37·S2) or kilocalories per minute (kcal·kg-1·min-1 = 308.8 + 105.2·S + 25.58·S2).

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.

Wearable-device-measured physical activity and future health risk

Use of wearable devices that monitor physical activity is projected to increase more than fivefold per half-decade1. We investigated how device-based physical activity energy expenditure (PAEE) and different intensity profiles were associated with all-cause mortality. We used a network harmonization approach to map dominant-wrist acceleration to PAEE in 96,476 UK Biobank participants (mean age 62 years, 56% female). We also calculated the fraction of PAEE accumulated from moderate-to-vigorous-intensity physical activity (MVPA). Over the median 3.1-year follow-up period (302,526 person-years), 732 deaths were recorded. Higher PAEE was associated with a lower hazard of all-cause mortality for a constant fraction of MVPA (for example, 21% (95% confidence interval 4-35%) lower hazard for 20 versus 15 kJ kg-1 d-1 PAEE with 10% from MVPA). Similarly, a higher MVPA fraction was associated with a lower hazard when PAEE remained constant (for example, 30% (8-47%) lower hazard when 20% versus 10% of a fixed 15 kJ kg-1 d-1 PAEE volume was from MVPA). Our results show that higher volumes of PAEE are associated with reduced mortality rates, and achieving the same volume through higher-intensity activity is associated with greater reductions than through lower-intensity activity. The linkage of device-measured activity to energy expenditure creates a framework for using wearables for personalized prevention.