Examines cognitive health monitoring in canines, suggesting improved veterinary care through binning methods.
The current Pet-Tech industry relies overwhelmingly on physical activity sensors as proxies for general animal health. This paper argues that the transformation of raw, asynchronous behavioral data into a predictive diagnostic tool for canine cognitive decline requires a structured temporal data architecture grounded in strategic binning — the aggregation of behavioral signals into hierarchically organized temporal intervals. Drawing on methods from clinical AI in human medicine and on individual cognitive profiling research at Duke University's Canine Cognition Center, this paper presents the Barkley framework: a precision medicine infrastructure that shifts veterinary health monitoring from static surveillance to dynamic cognitive chronology. The framework introduces three core innovations — multi-resolution temporal binning (Bin A: circadian daily pulse; Bin B: weekly baseline; Bin C: quarterly drift), rate-of-change feature engineering (Rate of Decay), and individual-referenced baseline adjustment — and articulates their implications for equitable, value-based veterinary care. Barkley White Paper | Precision Behavioral Intelligence Series | No. 01.
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Elodie P. Remoissenet (2026) studied this question.
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