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.

The introduction of a drone-based mobile sink into wireless sensor networks (WSNs), which has flexible mobility to move to each sensor node and gather data with a single-hop transmission, makes cumbersome multi-hop transmissions unnecessary, thereby facilitating data gathering from widely-spread sensor nodes. However, each sensor node spends significant amount of energy during their idle state where they wait for the mobile sink to come close to their vicinity for data gathering. In order to solve this problem, in this paper, we apply a wake-up receiver to each sensor node, which consumes much smaller power than the main radio used for data transmissions. The main radio interface is woken up only when the wake-up receiver attached to each node detects a wake-up signal transmitted by the mobile sink. For this mobile and on-demand data gathering, this paper proposes a route control framework that decides the mobility route for a drone-based mobile sink, considering the interactions between wake-up control and physical layer (PHY) and medium access control (MAC) layer operations. We investigate the optimality and effectiveness of the route obtained by the proposed framework with computer simulations. Furthermore, we present experimental results obtained with our test-bed of a WSN employing a drone-based mobile sink and wakeup receivers. All these results give us the insight on the role of wake-up receiver in mobile and on-demand sensing data gathering and its interactions with protocol/system designs.

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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.