949 resultados para Wearable, Internet-of-Things, Controllo accessi, Bluetooth, Apple Watch


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Edited by Emerson E. Ballard

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Mode of access: Internet.

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Mode of access: Internet.

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Long term recording of biomedical signals such as ECG, EMG, respiration and other information (e.g. body motion) can improve diagnosis and potentially monitor the evolution of many widespread diseases. However, long term monitoring requires specific solutions, portable and wearable equipment that should be particularly comfortable for patients. The key-issues of portable biomedical instrumentation are: power consumption, long-term sensor stability, comfortable wearing and wireless connectivity. In this scenario, it would be valuable to realize prototypes using available technologies to assess long-term personal monitoring and foster new ways to provide healthcare services. The aim of this work is to discuss the advantages and the drawbacks in long term monitoring of biopotentials and body movements using textile electrodes embedded in clothes. The textile electrodes were embedded into garments; tiny shirt and short were used to acquire electrocardiographic and electromyographic signals. The garment was equipped with low power electronics for signal acquisition and data wireless transmission via Bluetooth. A small, battery powered, biopotential amplifier and three-axes acceleration body monitor was realized. Patient monitor incorporates a microcontroller, analog-to-digital signal conversion at programmable sampling frequencies. The system was able to acquire and to transmit real-time signals, within 10 m range, to any Bluetooth device (including PDA or cellular phone). The electronics were embedded in the shirt resulting comfortable to wear for patients. Small size MEMS 3-axes accelerometers were also integrated. © 2011 IEEE.

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The growing ubiquity of smartphones and tablet devices integrated into personal, social and professional life, facilitated by expansive communication networks globally, has the potential to disrupt higher education. Academics and students are considering the future possibilities of exploiting these tools and utilising networks to consolidate and expand knowledge, enhancing learning gain. Bluetooth beacon technology has been developed by both Apple and Google as a way to situate digital information within physical spaces, and this paper reflects on a beacon intervention in a contemporary art school in higher education conducted by the authors intended to develop a situated community of practice in Art & Design. The paper describes the project, including relevant theoretical foundations and background to the beacon technology, with regards to the potential of using these devices to create a connected learning community by enhancing learning and facilitating knowledge creation in a borderless learning space.

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Photoplethysmography (PPG) sensors allow for noninvasive and comfortable heart-rate (HR) monitoring, suitable for compact wearable devices. However, PPG signals collected from such devices often suffer from corruption caused by motion artifacts. This is typically addressed by combining the PPG signal with acceleration measurements from an inertial sensor. Recently, different energy-efficient deep learning approaches for heart rate estimation have been proposed. To test these new solutions, in this work, we developed a highly wearable platform (42mm x 48 mm x 1.2mm) for PPG signal acquisition and processing, based on GAP9, a parallel ultra low power system-on-chip featuring nine cores RISC-V compute cluster with neural network accelerator and 1 core RISC-V controller. The hardware platform also integrates a commercial complete Optical Biosensing Module and an ARM-Cortex M4 microcontroller unit (MCU) with Bluetooth low-energy connectivity. To demonstrate the capabilities of the system, a deep learning-based approach for PPG-based HR estimation has been deployed. Thanks to the reduced power consumption of the digital computational platform, the total power budget is just 2.67 mW providing up to 5 days of operation (105 mAh battery).

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The aim of this study was to compare the performance of the following techniques on the isolation of volatiles of importance for the aroma/flavor of fresh cashew apple juice: dynamic headspace analysis using PorapakQ(®) as trap, solvent extraction with and without further concentration of the isolate, and solid-phase microextraction (fiber DVB/CAR/PDMS). A total of 181 compounds were identified, from which 44 were esters, 20 terpenes, 19 alcohols, 17 hydrocarbons, 15 ketones, 14 aldehydes, among others. Sensory evaluation of the gas chromatography effluents revealed esters (n = 24) and terpenes (n = 10) as the most important aroma compounds. The four techniques were efficient in isolating esters, a chemical class of high impact in the cashew aroma/flavor. However, the dynamic headspace methodology produced an isolate in which the analytes were in greater concentration, which facilitates their identification (gas chromatography-mass spectrometry) and sensory evaluation in the chromatographic effluents. Solvent extraction (dichloromethane) without further concentration of the isolate was the most efficient methodology for the isolation of terpenes. Because these two techniques also isolated in greater concentration the volatiles from other chemical classes important to the cashew aroma, such as aldehydes and alcohols, they were considered the most advantageous for the study of cashew aroma/flavor.

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A warning system for sooty blotch and flyspeck (SBFS) of apple, developed in the southeastern United States, uses cumulative hours of leaf wetness duration (LWD) to predict the timing of the first appearance of signs. In the Upper Midwest United States, however, this warning system has resulted in sporadic disease control failures. The purpose of the present study was to determine whether the warning system`s algorithm could be modified to provide more reliable assessment of SBFS risk. Hourly LWD, rainfall, relative humidity (RH), and temperature data were collected from orchards in Iowa, North Carolina, and Wisconsin in 2005 and 2006. Timing of the first appearance of SBFS signs was determined by weekly scouting. Preliminary analysis using scatterplots and boxplots suggested that Cumulative hours of RH >= 97% could be a useful predictor of SBFS appearance. Receiver operating characteristic curve analysis was used to compare the predictive performance of cumulative LWD and cumulative hours of RH >= 97%. Cumulative hours of RH >= 97% was a more conservative and accurate predictor than cumulative LWD for 15 site years in the Upper Midwest, but not for four site years in North Carolina. Performance of the SBFS warning system in the Upper Midwest and climatically similar regions may be improved if cumulative hours of RH >= 97% were substituted for cumulative LWD to predict the first appearance of SBFS.