By José Bravo, Ramón Hervás, Vladimir Villarreal
This ebook constitutes the refereed convention court cases of the 1st foreign convention on Ambient Intelligence for healthiness, AmIHEALTH 2015, held in Puerto Varas, Chile, in December 2015.
The 20 revised complete papers and nine brief papers have been reviewed and chosen from 32 submissions and canopy subject matters on applied sciences for enforcing AmIHealth environments; frameworks similar with AmIHealth environments; utilized algorithms in e-Health platforms; interactions in the AmIHealth environments; functions and case experiences of AmIHealth environments; and metrics for well-being environments.
Read Online or Download Ambient Intelligence for Health: First International Conference, AmIHEALTH 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings PDF
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Additional info for Ambient Intelligence for Health: First International Conference, AmIHEALTH 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings
The electronic layer is built using industrial rigi-flex technology what allows the required degree of flexibility of the insole (Fig. 3). The components selected for the electronics layer are: Fig. 3. g. step length, step frequency). • A MCU to control the acquisition and ADC for the pressure sensors. • The main SoC (System-on-Chip) that includes a microprocessor and the Bluetooth Low Energy (BLE) core that relays sensors data to the smartphone. 7 V and 200 mAh). • Embedded coil to power the battery through inductive coupling from a Qi charger.
AmIHEALTH 2015, LNCS 9456, pp. 26–36, 2015. 1007/978-3-319-26508-7_3 A Mobile Cloud Shared Workspace to Support Homecare 27 variety of interventions that are related to airway management and maintenance of lung health. These include oxygen therapy, ventilation, tracheostomy care, medication management, and teaching of and support with inhaler-use techniques. Eligible patients can be funded fully or partially to receive RT home visits for home oxygen, which is usually indicated during some stage of the disease.
The classes for this problem were: super obesity (S), morbid obesity (M), severe obesity (SO), and moderate obesity (MO). To generate a gold standard for classiﬁcation, we asked two students with biomedical background to revise and annotated a total of 2462 records using an annotation tool designed in QT-designer1 and programmed in Python. This tool allows the annotator to assign a speciﬁc label to a record and suggest possible features for classiﬁcation. For each record, they ﬁrst assigned a label within the ﬁrst classiﬁcation problem.
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