Special Issue on Body Area Networks

info:eu-repo/semantics/publishedVersio

Research and Development of a Cyber-I Open Service Platform

研究成果の概要 (和文) : Cyber-Iは、Real-Iのデジタル対応であり、個人データの収集と分析を行い、人の行動や感情に近づけます。本研究では、複数のデバイスから多くの個人データを収集して処理を行い、Cyber-Iの作成と管理を行うようなオープンサービスプラットフォームを開発しました。異なるデバイスやデータを柔軟でスケーラブルな管理をするために、スマートフォンをゲートウェイとして使用するクラウドやフォグベースのデータベースシステムを実装しています。また、Cyber-Iの成長をコントロールするような基本的技術やメカニズムを提案しています。さらにCyber-I関連における個人情報の保護と利用についても検討しています。研究成果の概要 (英文) : Cyber-I, short for Cyber Individual, is a digital counterpart of Real-Individual (Real-I), and is expected to continuously approximate a real person’s behavior and even mind with collections and analyses of increasing personal data. In this research, a Cyber-I open service platform has been researched and developed to collect and process rich personal big data from various sources and multiple devices for Cyber-I creation and administration as well as its modeling and life control. A cloud-fog based database system using smartphones as gateways has been implemented for flexible and scalable managements of heterogeneous devices and data. Basic strategy and mechanism have been proposed for scheduling and controlling Cyber-I growth. Cyber-I related data privacy protection and personal information usage are also studied. A series of researches on personality and affective computing has been carried out to model personal characteristics

Analysis and Modeling of Temporal Features in Data Streams from Multiple Wearable Devices

Time is a vitally important issue in the coordination of multiple wearable devices. Theoretically, wearable applications should require data streams to be synchronized with the necessary degree of precision. However, in the available applications, this critical issue has not been well considered. Actually, time discrepancies exist among data streams, resulting in certain decrease of data analysis and fusion accuracy. The study of time discrepancy is rarely found in the literature, and there is no specific model to describe temporal features. In this dissertation, we first analyze several temporal issues in multi-wearable system and the source of time discrepancy. Then, by taking into account temporal features, we propose two typical models, which provide statistical methods for describing time discrepancy and its distribution. Furthermore, the accuracy of the models is verified by a set of experiments. Finally, we demonstrate the application of the proposed models through a case study, in which the adaptive frequency strategy is adopted. Experimental results show that the strategy can not only guarantee the completeness of the data, but also reduce redundancy compared with the static frequency method. Our models and experiments of time discrepancy can be a basis for further research on the time synchronization of data from multiple wearable devices