Cognitive Drift Mapping Using Ambient Micro-Behavioral IoT Signals in Preclinical Neurodegeneration

The chapter introduces the Cognitive Drift Mapping that is a longitudinal and entropy-based approach to detecting preclinical neurodegeneration using ambient micro-behavioral IoT signals. The methodology is the description of gradual changes in daily activities in behavioral patterns such as gait speed, speech response time, typing reaction, navigation volume, and sleep instability instead of episodic clinical assessment or constant limiting values. The Sliding-window entropy estimation and change-point detection transforms the incoming sensor data (as continuous data) to discrete drift curves. The federated learning and edge AI models have the capability of delivering privacy guarantees in terms of low-latency analytics by processing the features locally and transmitting encrypted parameters only. It serves to encourage early, decipherable, and preventive neurocognitive intelligence in sustainable aging in place since it emphasizes intra-individual baselines and multimodal fusion.