By José Bravo, Ramón Hervás, Vladimir Villarreal
This ebook constitutes the refereed convention court cases of the 1st foreign convention on Ambient Intelligence for well-being, 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 issues on applied sciences for enforcing AmIHealth environments; frameworks similar with AmIHealth environments; utilized algorithms in e-Health structures; interactions in the AmIHealth environments; purposes and case reports of AmIHealth environments; and metrics for healthiness 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 resources for Ambient Intelligence for Health: First International Conference, AmIHEALTH 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings
In the case of NB, we use a multinomial approach together with TF-IDF matrix representation. In the case of SVM, we used a linear kernel together with the one by one multiclass classiﬁcation setting, the rest of the parameters were kept at their default values. To evaluate the classiﬁcation models, we implemented ten-fold cross validation and repeated each experiment 10 times. Performance measures used to evaluate the classiﬁers’ predictive capacity were Accuracy (ACC), F-measure, False Positive Rate (FPR) and False Negative Rate (FNR).
Patients with no means of mobilization could keep their treatments unaltered and new users could be included. Acknowledgements. This work was partially supported by VRI-PUC Interdisciplinary Grant No. 03/2013 and UC Center for Public Policies Grant 2015. Finally, we’d like to give special thanks to Claudio Jerez, CEO of TEVEUK Ltd, for providing the connection with homecare patients. A Mobile Cloud Shared Workspace to Support Homecare 35 References 1. Minsal prepara planes para tratar efectos postraumáticos en vecinos del volcán calbuco, el mercurio online (2015).
Example of a ﬁnal insole in the charging station insole or communications system, summary of the user’s gait and activity, such as steps per day counts, distance measurements and the current fall risk (customisable), and a fall detection system interface. The Smartphone is used to collect data wirelessly from the insoles using the Bluetooth Low Energy standard. 0 or above and therefore have built-in Bluetooth low-energy communication. Incoming data is processed locally at reception in order to detect falls.