Many researches for useful status information on humans have been done using the bio-signals. The bio-signal acquisition systems can be used to connect a user and a ubiquitous computing environment. The ubiquitous computing environment has to give various services anywhere, anytime. Consequently, ubiquitous computing requires new technology, such as a new user interface, dynamic service mechanism based on context and mobility support, which is different from technology used in desktop environment. To do this, we developed a wearable system, which can sense physiological data, determine emotional status and execute service based on the emotion. In this paper, we described wearable systems for personalized service based on physiological signals. The wearable system is composed of three subsystems, the physiological data sensing subsystem, the human status awareness subsystem and the service management subsystem. The physiological data sensing subsystem senses PPG, GSR and SKT signals from the data glove and sends the data to a wearable system using Bluetooth. The human status awareness subsystem in the wearable system receives the data from bio-sensors and determines emotional status using nonlinear mapping and rule-base. After determining emotion, the service management subsystem activates proper service automatically, and the service management subsystem can provide personalized service for users based on acquired bio-signals. Also, we presented various feature extraction using bio-signals such as PPG, GSR, SKT considering mobility, and emotion recognition of human status for the ubiquitous computing service.
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http://dx.doi.org/10.1109/IEMBS.2005.1616961 | DOI Listing |
BMC Genomics
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Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 610225, China.
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January 2025
Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for prediction and classification problems.
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January 2025
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
This article presents a systematic review on blockchain-facilitated cybersecurity solutions for Internet of Things (IoT) devices in space-air-ground integrated networks (SAGIN). First, we identify the objectives and the context of the blockchain-based solutions for SAGIN. Although, typically, the blockchain is primarily used to enhance the trustworthiness of some systems or operations, it is necessary to document exactly in what context the blockchain is used that is specific to the IoT and SAGIN.
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December 2024
Department of Biological Sciences, Clemson University, Clemson, SC 29634, USA.
Per- and polyfluoroalkyl substances (PFAS) are nearly ubiquitous and found in rivers, soils, atmosphere, food packaging, clothing, cosmetics, commercial products, homes, drinking water, and humans and other organisms [...
View Article and Find Full Text PDFEntropy (Basel)
January 2025
Department of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Turin, Italy.
Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields. Their statistical characterization is crucial to both design guideline formulation and efficient computation of performance indices. To this end, random matrix theory can be successfully exploited.
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