A Comprehensive Review of Alzheimer’s Patient Localization Using Adaptive Dual-Channel Pulse-Coupled Neural Networks in Wireless Sensor Networks

Abstract

Alzheimer’s disease is a progressive neurological disorder that affects memory, cognition, and spatial awareness, often leading to wandering behavior and increased safety risks. Accurate localization of Alzheimer’s patients is essential for ensuring their safety and improving healthcare monitoring systems. This paper presents a comprehensive review of patient localization techniques using Wireless Sensor Networks (WSNs) integrated with Adaptive Dual-Channel Pulse-Coupled Neural Networks (PCNN). WSN-based systems utilize sensor nodes and RSSI-based measurements to estimate patient positions in indoor environments where GPS is ineffective.

Recent advancements in machine learning and deep learning have significantly enhanced localization accuracy by addressing issues such as signal noise and environmental interference. Adaptive Dual-Channel PCNN improves feature extraction and noise reduction by processing multi-source data simultaneously, leading to more reliable localization outcomes. This review analyzes studies from 2020 to 2023, comparing approaches such as ANN, CNN, LSTM, and hybrid models.

The paper highlights improvements in accuracy, scalability, and energy efficiency while identifying challenges such as energy consumption, privacy concerns, and deployment complexity. Future research directions include the integration of edge computing and lightweight AI models for efficient and secure patient localization.