Mobile Device Passive Data Analysis for Cognitive Decline Detection
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Solution Overview
Problem
Current methods for detecting cognitive decline, such as neuropsychological tests, require active participation and specialized personnel, making early diagnosis challenging and limited by rater bias, cultural bias, and the need for lengthy testing, which impedes widespread screening.
Innovation Solution
The use of mobile devices to passively collect data on user behavior and physiological parameters over an observation period, generating digital biomarkers to analyze for cognitive decline without requiring active engagement or specialized intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neuropsychological tests are used to detect cognitive decline, then measurement precision is improved, but device complexity and ease of operation worsen due to requiring specialized personnel and active participation
Solution Approach 1:
The system uses mobile devices that subjects already possess and use daily, eliminating the need for specialized testing equipment. The device performs self-monitoring of behavioral patterns without requiring external specialized personnel or complex medical equipment.
Solution Approach 2:
The patent replaces mechanical/neuropsychological testing systems with a digital information-processing system. Instead of using specialized personnel to administer and interpret physical tests, the system uses software to automatically collect, process, and analyze mobile device data to detect cognitive decline.
2Measurement precision
If neuropsychological tests are used to detect cognitive decline, then measurement precision is improved, but loss of time worsens due to lengthy testing requirements
Solution Approach 1:
The system continuously collects behavioral data in the background during normal daily activities before cognitive decline becomes apparent. This preliminary data accumulation allows for early detection without requiring time-consuming testing sessions when symptoms are already evident.
Solution Approach 2:
Instead of periodic lengthy tests, the system continuously monitors behavioral patterns over time. The mobile device automatically tracks messaging habits, movement patterns, and other behaviors continuously, providing ongoing assessment without interrupting the subject's daily routine.
3Measurement precision
If neuropsychological tests are used to detect cognitive decline, then measurement precision is improved, but ease of operation worsens due to requiring active participation
Solution Approach 1:
The system leverages mobile devices that subjects already use independently in their daily lives. The device automatically monitors behavioral patterns without requiring the subject to actively participate in testing or interact with medical personnel, making the process as effortless as normal device usage.
4Ease of operation
If mobile devices are used for passive data collection, then ease of operation is improved, but measurement precision worsens due to potential rater bias and cultural bias
Solution Approach 1:
The system replaces human raters with automated algorithms that objectively analyze mobile device data. This substitution eliminates rater bias and cultural bias by using consistent computational methods to interpret behavioral patterns, ensuring standardized assessment across diverse populations.
Solution Approach 2:
The system continuously compares individual behavioral patterns against established norms and provides feedback for model refinement. This iterative feedback mechanism improves detection accuracy over time by learning from diverse populations and adjusting algorithms to account for individual variations without introducing bias.
Data Source
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AI summary
Embodiments of the present disclosure relate systems and methods for detecting cognitive decline of a subject using passively obtained data from at least one mobile device. In an exemplary embodiment, a computer-implemented method comprises receiving passively obtained data from at least one mobile device. The method further comprises generating digital biomarker data from the passively obtained data. The method further comprises analyzing the digital biomarker data to determine whether the subject is exhibiting signs of cognitive decline.