Setting Up Our Lab-in-a-Box: Paving the Road Towards Remote Data Collection for Scalable Personalized Biometrics.

Area of Science:

• Neuroscience
• Biophysics
• Digital Health

Background:

• Growing need for scalable, at-home behavioral assays beyond traditional clinical settings.
• Challenges in data acquisition and analysis for diverse populations in remote research.
• Opportunity to leverage citizen science for research and clinical practice.

Purpose of the Study:

• Develop and validate scalable, home-based methods for behavioral data acquisition and analysis.
• Characterize biophysical fluctuations for behavioral state assessment.
• Create standardized biometric scales for neurological disorder screening across the lifespan.

Main Methods:

• Utilized standardized biorhythmic time series data (kinematic, ECG, voice, video) in naturalistic settings.
• Employed a functional taxonomy for organizing data by nervous system maturation/decline.
• Developed a unifying statistical platform for individualized behavioral analysis.

Main Results:

• Characterized digital ranges of biophysical signals in neurotypical individuals.
• Identified departures from normative ranges in neurodevelopmental and neurodegenerative disorders.
• Demonstrated data reduction to a single Gamma scale parameter for participant classification.

Conclusions:

• Highlighted methodological and analytical techniques for personalized, scalable biometrics.
• Emphasized the feasibility of developing these techniques outside laboratory settings.
• Proposed new avenues for scaling research, education, and clinical practices.