Ambient Assisted Living for Enhanced Elderly and Differently Abled Care: A Novel Attention Transfer Learning-based Crossover Chimp Optimization

Abstract

Ambient assisted living (AAL) is a groundbreaking approach that harnesses the power of smart technology to offer all-encompassing care and support for elderly and differently abled individuals in their day-to-day lives. Progressive innovation in AAL solutions can facilitate and support day-to-day routines, expanding the time they can live autonomously and supporting proficiency. This research mainly analyzes AAL’s significant role in tending to the exceptional difficulties these populations face. AAL frameworks incorporate an array of sensors, gadgets, and intelligent calculations that help monitor current circumstances and exercises, empowering early recognition of peculiarities, fall counteraction, and customized help. This research introduces a novel attention transfer learning-based crossover chimp (ATL-CC) algorithm for AAL, which combines crossover-based chimp optimization with a transformer-based model for transfer learning, integrating an attention mechanism. The ATL-CC algorithm aims to enhance activity recognition and classification within AAL environments. Precision, accuracy, recall, root mean square error, and F1-score are evaluated, where accuracy attains the value of 98.9%, precision attains the value of 97.4%, recall attains the value of 98%, and F1-score attains the value of 96%. Overall, AAL arises as a promising arrangement that upholds the deprived and advances respect, independence, and inclusivity in maturing and various societies.