The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,G...
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The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.
The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy,...
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The last technological advances in wearable sensors and machine learning are allowing for a new generation of human monitoring techniques, with an especial interest for the analysis of human biomechanics and activity ...
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ISBN:
(纸本)9781601321855
The last technological advances in wearable sensors and machine learning are allowing for a new generation of human monitoring techniques, with an especial interest for the analysis of human biomechanics and activity recognition. In this paper, an application of intelligent systems to solve the problem of daily physical activity recognition is presented. Taking into account the importance of data featuring and the selection of the most important features for subsequent pattern recognition stage, a new feature selection methodology based on a filter technique via a couple of two statistical criteria is presented. Satisfactory accuracy rates are achieved by using support vector machines especially for preprocessed data, with remarkable accuracy and applicability in the case of the wrist location.
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