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smartphone and wearable data


Extraction of Behavioral Features from Smartphone and Wearable Data

arXiv.org Machine Learning

Mobile phones and wearable devices are now equipped with powerful sensors that allow us to collect information about a user's context and behaviors, including location, communication, environment, phone usage, physical activity and sleep. Existing research has explored the capability of mobile phones and fitness trackers to continuously trackand collect information about the daily behavior of users through their sensing channels and to use this data to analyze the state ofphysical and mental wellbeing suchas sleep duration and quality(Min et al. 2014) and depression( (Doryab et al. 2014; Saeb et al. 2016, 2015)). The widespread use of such devices for research in the ubiquitous and mobile computing communities including pervasive and mobile health as well as their common set of sensing channels gives rise to providing a comprehensive andgeneric framework for data collection and processing that can be shared in the research community. Such framework has the following advantages: - It provides a generic tool for mobile data processing that can be easily used or adapted by other researchers.


Scientists use AI to predict biological age based on smartphone and wearables data

#artificialintelligence

Researches at longevity biotech company GERO and Moscow Institute of Physics and Technology have developed a computer algorithm that uses Artificial Intelligence to predict biological age and the risk of mortality based on physical activity. The paper is published in Scientific Reports.


Scientists use AI to predict biological age based on smartphone and wearables data

#artificialintelligence

IMAGE: This is a screenshot of the Gero Lifespan app. Moscow, March 29, 2018 - Researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT) have shown that physical activity data acquired from wearables can be used to produce digital biomarkers of aging and frailty. Many physiological parameters demonstrate tight correlations with age. Various biomarkers of age, such as DNA methylation, gene expression or circulating blood factor levels could be used to build accurate «biological clocks» to obtain individual biological age and the rate of aging estimations. Yet large-scale biochemical or genomic profiling is still logistically difficult and expensive for any practical applications beyond academic research.