Titlebook: Activity and Behavior Computing; Md Atiqur Rahman Ahad,Sozo Inoue,Kaori Fujinami Book 2021 The Editor(s) (if applicable) and The Author(s)

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https://doi.org/10.57088/978-3-7329-8912-6 is estimated from each captured image using OpenPose. To solve the asynchrony problem, we virtually generate the synchronous pose data by interpolating the temporally neighboring poses that are from actually captured images. Then, the 3D pose is reconstructed by triangulation from the virtually syn

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,Grundlagen automatisierter Überwachung,recognition is a time-consuming task, therefore methods which reduce the amount of labelled data required to train accurate classifiers are important. Generative Adversarial Networks (GANs) can be used to model the implicit distribution of a dataset. Traditional GANs, which only consist of a generat

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https://doi.org/10.1007/978-3-642-82061-8xisting on-body device localization methods deal with a fixed number of positions. In contrast, we have proposed a framework to discover new positions that are not initially supported by the system and add them as recognition targets during use. In this paper, we focus on the task of detecting new c

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Schlussfolgerungen und Ausblick,nd only on inertial measurement unit (IMU) signals is needed, in this study, we propose a simpler signal feature-based method for detecting gait events that is feasible for use in in-shoe motion sensor (IMS) systems, and these exact features are used to determine the timings of IC and TO according t

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https://doi.org/10.1007/978-3-658-27990-5s such as the number of chewing and the duration of the conversation leads to positive dietary behavior. This paper proposes a method that can accurately quantify eating behavior in a natural meal environment. We used a bone conduction microphone and recorded the dietary sounds of 16 subjects. We ma

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