Tri-axial high-resolution acceleration for oxygen uptake estimation: Validation of a multi-sensor device and a novel analysis method

WSN Hardware for Automotive Applications: Preliminary Results for the Case of Public Transportation

The ubiquitous nature and great potential of Wireless Sensors Network has not yet been fully exploited in automotive applications. This work deals with the choice of the cost-effective hardware required to face the challenges and issues proposed by the new trend in the development of intelligent transportation systems. With this aim, a preliminary WSN architecture is proposed. Several commercially available open-source platforms are compared and the Raspberry Pi stood out as a suitable and viable solution. The sensing layer is designed with two goals. Firstly, accelerometric, temperature, and relative humidity sensors were integrated on a dedicated PCB to test if mechanical or environmental stresses during bus rides could be harmful to the device or to its performances. The physical quantities are monitored automatically to alert the driver, thus improving the quality of service. Then, the rationale and functioning of the management and service layer is presented. The proposed cost-effective WSN node was employed and tested to transmit messages and videos, while investigating if any quantitative relationship exists between these operations and the environmental and operative conditions experienced by the hardware.

Energy expenditure associated with walking speed and angle of turn in children

Purpose

Recent studies have suggested that turning is power intensive. Given the sporadic and irregular movement patterns of children, such findings have important implications for the assessment of true energy expenditure associated with habitual physical activity. The purpose of this study was to investigate the influence of walking speed and angle, and their interaction, on the energy expenditure of healthy children.

Methods

20 children (10.1 ± 0.5 years; 10 boys) participated in the study. On two separate days, participants completed a turning protocol involving 3-min bouts of walking at one of the 16 speed (2.5, 3.5, 4.5, and 5.5 km h^− 1) and angle (0°, 45°, 90°, and 180°) combinations, interspersed by 3 min seated rest. The movement involved 5 m straight walking interspaced with prescribed turns with speed dictated by a digital, auditory metronome. Breath-by-breath gas exchange was measured, in addition to tri-axial acceleration and magnetic field intensity recorded at 100 Hz.

Results

Mixed models revealed a significant main effect for speed (p < 0.006) and angle (p < 0.006), with no significant interaction between speed and angle (p > 0.006). Significant differences to straight-line walking energy expenditure within speed were established for 3.5 and 5.5 km h^− 1 for 180° turns (~ 13% and ~ 30% increase, respectively).

Conclusion

These findings highlight the importance of accounting for the magnitude and frequency of turns completed when estimating children’s habitual physical activity and have significant implications for the assessment of daily energy expenditure.

Multi-dimensional Precision Livestock Farming: a potential toolbox for sustainable rangeland management

BACKGROUND

Precision Livestock Farming (PLF) is a promising approach to minimize the conflicts between socio-economic activities and landscape conservation. However, its application on extensive systems of livestock production can be challenging. The main difficulties arise because animals graze on large natural pastures where they are exposed to competition with wild herbivores for heterogeneous and scarce resources, predation risk, adverse weather, and complex topography. Considering that the 91% of the world's surface devoted to livestock production is composed of extensive systems (i.e., rangelands), our general aim was to develop a PLF methodology that quantifies: (i) detailed behavioural patterns, (ii) feeding rate, and (iii) costs associated with different behaviours and landscape traits.

METHODS

For this, we used Merino sheep in Patagonian rangelands as a case study. We combined data from an animal-attached multi-sensor tag (tri-axial acceleration, tri-axial magnetometry, temperature sensor and Global Positioning System) with landscape layers from a Geographical Information System to acquire data. Then, we used high accuracy decision trees, dead reckoning methods and spatial data processing techniques to show how this combination of tools could be used to assess energy balance, predation risk and competition experienced by livestock through time and space.

RESULTS

The combination of methods proposed here are a useful tool to assess livestock behaviour and the different factors that influence extensive livestock production, such as topography, environmental temperature, predation risk and competition for heterogeneous resources. We were able to quantify feeding rate continuously through time and space with high accuracy and show how it could be used to estimate animal production and the intensity of grazing on the landscape. We also assessed the effects of resource heterogeneity (inferred through search times), and the potential costs associated with predation risk, competition, thermoregulation and movement on complex topography.

DISCUSSION

The quantification of feeding rate and behavioural costs provided by our approach could be used to estimate energy balance and to predict individual growth, survival and reproduction. Finally, we discussed how the information provided by this combination of methods can be used to develop wildlife-friendly strategies that also maximize animal welfare, quality and environmental sustainability.

VO2 estimation using 6-axis motion sensor with sports activity classification

In this paper, we focus on oxygen consumption (VO2) estimation using 6-axis motion sensor (3-axis accelerometer and 3-axis gyroscope) for people playing sports with diverse intensities. The VO2 estimated with a small motion sensor can be used to calculate the energy expenditure, however, its accuracy depends on the intensities of various types of activities. In order to achieve high accuracy over a wide range of intensities, we employ an estimation framework that first classifies activities with a simple machine-learning based classification algorithm. We prepare different coefficients of linear regression model for different types of activities, which are determined with training data obtained by experiments. The best-suited model is used for each type of activity when VO2 is estimated. The accuracy of the employed framework depends on the trade-off between the degradation due to classification errors and improvement brought by applying separate, optimum model to VO2 estimation. Taking this trade-off into account, we evaluate the accuracy of the employed estimation framework by using a set of experimental data consisting of VO2 and motion data of people with a wide range of intensities of exercises, which were measured by a VO2 meter and motion sensor, respectively. Our numerical results show that the employed framework can improve the estimation accuracy in comparison to a reference method that uses a common regression model for all types of activities.

Applying neural network to VO2 estimation using 6-axis motion sensing data

This paper focuses on oxygen consumption (VO2) estimation using 6-axis motion data (3-axis acceleration and 3-axis angular velocity) that are obtained from small motion sensors attached to people playing sports with different intensities. In order to achieve high estimation accuracy over a wide range of intensities of exercises, we apply neural network that is trained by experimental data consisting of the measured VO2 and motion sensing data of people with a wide range of intensities of exercises. We first investigate the gain brought by applying neural network by comparing its accuracy with an approach based on the linear regression model. Then, we analyze how much improvement the information on angular velocity can bring as compared with the estimation with the acceleration data alone. Our numerical results show that the employed framework exploiting neural network can improve the estimation accuracy in comparison to the linear regression model and the exploitation of information on the angular velocity plays an important role to improve the accuracy over higher intensities of exercises.

Physiological Demands of Flat Horse Racing Jockeys

The physiological demands of jockeys during competition remain largely unknown, thereby creating challenges when attempting to prescribe sport-specific nutrition and training guidelines. The purpose of this study was to evaluate the physiological demands and energy requirements of jockeys during flat racing. Oxygen uptake (V[Combining Dot Above]O2) and heart rate (HR) were assessed in 18 male trainee jockeys during a race simulation trial on a mechanical horse racing simulator for the typical time duration to cover a common flat race distance of 1,400 m. In addition, 8 male apprentice jockeys participated in a competitive race, over distances ranging from 1,200 to 1,600 m, during which HR and respiratory rate (RR) were assessed. All participants performed a maximal incremental cycle ergometer test. During the simulated race, peak V[Combining Dot Above]O2 was 42.74 ± 5.6 ml·kg·min (75 ± 11% of V[Combining Dot Above]O2peak) and below the mean ventilatory threshold (81 ± 5% of V[Combining Dot Above]O2peak) reported in the maximal incremental cycle test. Peak HR was 161 ± 16 b·min (86 ± 7% of HRpeak). Energy expenditure was estimated as 92.5 ± 18.8 kJ with an associated value of 9.4 metabolic equivalents. During the competitive race trial, peak HR reached 189 ± 5 b·min (103 ± 4% of HRpeak) and peak RR was 50 ± 7 breaths per minute. Results suggest that horse racing is a physically demanding sport, requiring jockeys to perform close to their physiological limit to be successful. These findings may provide a useful insight when developing sport-specific nutrition and training strategies to optimally equip and prepare jockeys physically for the physiological demands of horse racing.