DC++ 0.881 download the new version for android

The COVID-19 outbreak has pushed health authorities to fight an unprecedented battle against the time. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions.

Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. The distance measurement accuracy heavily affects the probability estimation of being infected. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general.

DC++ 0.881 download the new version for android