Socialife develops a proprietary system that extracts and analyzes digital action data and creates algorithms based on cognitive data and translates them into prediction models for behavioral outcomes source to define sets of rules drawn from neuroscience. Socialife's DIGITAL BIOMARKERS help to characterize real world behaviors from computer game play In order to identify styles of play that can indicate a risk for ASD’s and other cognitive disorders. Similar to Genetic Biomarkers, we identify digital sequences, which provide indicators, early on, of cognitive deficits. By analyzing behavior while people play games and are online for recreational pursuits, we obtain longitudinal data and performance measures that highlight patterns of change over time and reflects ongoing, real world behavior. On-line game environments contain a considerable amount of latent health-relevant behavioral data. Computer game performance characteristics provide both diagnostics screening information, as w...
Socialife develops a proprietary system that extracts and analyzes digital action data and creates algorithms based on cognitive data and translates them into prediction models for behavioral outcomes source to define sets of rules drawn from neuroscience. Socialife's DIGITAL BIOMARKERS help to characterize real world behaviors from computer game play In order to identify styles of play that can indicate a risk for ASD’s and other cognitive disorders. Similar to Genetic Biomarkers, we identify digital sequences, which provide indicators, early on, of cognitive deficits. By analyzing behavior while people play games and are online for recreational pursuits, we obtain longitudinal data and performance measures that highlight patterns of change over time and reflects ongoing, real world behavior. On-line game environments contain a considerable amount of latent health-relevant behavioral data. Computer game performance characteristics provide both diagnostics screening information, as well as identification of clinically relevant patterns of behavioral interval change over time. By increasing the availability of ASD testing at home, without the need of a trained examiner or computerized testing, we score subjects by analyzing computerized game performance from games that subjects find enjoyable and engage in as recreational activities. By integrating a proprietary API with popular games that are in widespread recreational use in social media environments, we record actions such as; mouse clicks, time between clicks, cursor positions, and many others. Combining these with demographic data provides information to characterize memory, attention to detail, concentration, and general game “awareness”. The data constructs gleaned from games themselves can be derived from distinct games that involving comparable performance aspects. This feature is critical since over periods of time longer than those being investigated in the present project, subjects will play games for a limited period of time and then change to new ones as novelty of the original game diminishes. Multiple shared features of game playing behavior that are not game-specific allow performance characterization over extended time periods. By utilizing daily, real-world behaviors, over extended time periods, we identify patterns of longitudinal change that may be clinically relevant and access performance measures that may be more sensitive to changes in neuro-cognitive function.
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