The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild. The raw data streams, however, can rarely be directly used in the modeling pipeline. We provide a generic framework that can process raw data streams and extract useful features related to non-verbal human behavior. This framework can be used by researchers in the field who are interested in processing data from smartphones and Wearable devices.

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.

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.