A Multi-Agent and Attention-Aware Enhanced CNN-BiLSTM Model for Human Activity Recognition for Enhanced Disability Assistance.

Artificial intelligence (AI)-based automated human activity recognition (HAR) is essential in enhancing assistive technologies for disabled individuals, focusing on fall detection, tracking rehabilitation progress, and analyzing personalized movement patterns. It also significantly manages and grows multiple industries, such as surveillance, sports, and diagnosis. This paper proposes a novel strategy using a three-stage feature ensemble combining deep learning (DL) and machine learning (ML) for accurate and automatic classification of activity recognition.