This proposal focuses on a new statistical platform paired with wearable sensing technology that objectively and automatically assesses real-time changes in the intentional and spontaneous aspects of physical motions embedded in natural human behaviors. The proposed activity will also help the team to better customize the behavioral motion data collection and analysis to address different needs of potential customers. Such customization can lead to more insight on various scientific questions involving the interconnections between brain and body functions during naturalistic behaviors, behavioral and pharmacological interventions, as well as in sports and performance training.
The proposed activity (Statistical Platform for Individualized Behavioral Analyses, SPIBA) addresses two fundamental gaps in current analytical techniques for motion capture data: (1) The lack of real time capability within physiologically relevant time scales and frequencies; (2) The reliance on expected values from population averages rather than from the individual's inherent physiological features. SPIBA does not assume the Theoretical Gaussian distribution or homogeneous variance of motion parameters. Instead, the underlying probability distributions of various motion trajectory parameters along with their real time shifts in noise to signal ratios are estimated. These estimates and their rates of change are unique to the individual and therefore amenable to develop treatment effectiveness metrics for truly personalized medicine.
