Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints.

In the fields of professional and amateur sports, players' health, physical and physiological conditions during exercise should be properly monitored and managed. The authors of this paper previously proposed a real-time vital-sign monitoring system for players using a wireless multi-hop sensor network that transmits their vital data. However, existing routing schemes based on the received signal strength indicator or global positioning system do not work well, because of the high speeds and the density of sensor nodes attached to players. To solve this problem, we proposed a novel scheme, image-assisted routing (IAR), which estimates the locations of sensor nodes using images captured from cameras mounted on unmanned aerial vehicles. However, it is not clear where the best viewpoints are for aerial player detection. In this study, the authors investigated detection accuracy from several viewpoints using an aerial-image dataset generated with computer graphics. Experimental results show that the detection accuracy was best when the viewpoints were slightly distant from just above the center of the field. In the best case, the detection accuracy was very good: 0.005524 miss rate at 0.01 false positive-per-image. These results are informative for player detection using aerial images and can facilitate to realize IAR.

Effect of Position and Fastening Belt Pressure on the Accuracy of PPG-Based Heart Rate Sensor.

Photoplethysmography (PPG) is simple, non-invasive and unobtrusive, so it has been commonly used for heart rate (HR) sensing during exercise. PPG-based HR sensor can be applied for almost any part of human body where there is an artery close to the sensor, just by stabilizing it on the skin surface with belt. However, in order to make the HR sensor stable even during vigorous exercise, it requires a high fastening belt pressure, which results in discomfort for the sensor wearer. In this paper, we investigate the relationship between fastening belt pressure and accuracy for PPG-based HR sensors put on two positions such as forearm and wrist. First of all, we conducted a preliminary experiment using 10 subjects to associate fastening belt pressure (from 10hPa to 90hPa) to human comfort or discomfort (loose, moderate, tight, and very tight). Then, we conducted an experiment to measure HR for 10 subjects during exercise, changing the belt pressure and exercise intensity. Experimental results reveal that the forearm position gives higher accuracy for HR sensing than the wrist position, however, exercise severely introduces motion artifact (MA) even for the for earm position. Therefore, if we want to achieve higher accuracy during exercise with moderate fastening belt pressure, a technique to cancel MA is required even for forearm-type PPG-based HR sensors.

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.