Long-Term Temporal Segmentation and Genotype-Aware Behavioral Phenotyping for AI-Driven Analysis of Sleep–Metabolism Interactions
Keywords:
temporal segmentation, genotype-aware phenotyping, sleep, metabolism, artificial intelligence, system architecture, data governance, fairness, sustainabilityAbstract
The bidirectional interplay between sleep architecture and metabolic homeostasis is increasingly recognized as a critical axis of human health, yet its systematic analysis at scale remains limited by methodological fragmentation. This paper presents a systems-level framework that integrates long-term temporal segmentation of multimodal behavioral signals with genotype-aware phenotyping, leveraging contemporary artificial intelligence to dissect sleep–metabolism interactions. We examine the structural trade-offs inherent in deploying memory-efficient video object segmentation models for continuous behavioral state parsing across weeks or months, and we discuss the fusion of such temporally resolved outputs with population-scale genomic data, including the influence of single-nucleotide polymorphisms on metabolic and sleep traits. The discussion centers on architectural design, governance, infrastructure, robustness, fairness, and sustainability rather than on specific algorithmic formulations. We argue that a holistic socio-technical approach is required to reconcile the technical challenges of long-sequence modeling with the ethical imperatives of genetic data stewardship, cross-population fairness, and environmental impact. Policy implications for clinical translation, consumer wellness platforms, and public health monitoring are explored, emphasizing the need for transparent, auditable systems that resist overdetermination by biased training distributions. Through a synthesis of recent advances in computer vision, genomics, and sleep science, we offer a forward-looking perspective on building resilient, equitable, and environmentally conscious AI systems for behavioral phenotyping.
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