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

Background: 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. Methods: 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. We develop a unique activity detection approach in this study by enhancing the state-of-the-art convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) models with selective ML classifiers and an attention mechanism. Thus, we developed an ensemble activity recognition model, namely “Attention-CNN-BiLSTM with selective ML”. Results: Out of the nine ML models and four DL models, the top performers are selected and combined in three stages for feature extraction. The effectiveness of this three-stage ensemble strategy is evaluated utilizing various performance metrics and through three distinct experiments. Utilizing the publicly available datasets (i.e., the UCI-HAR dataset and WISDM), our approach has shown superior predictive accuracy (98.75% and 99.58%, respectively). When compared with other methods, namely CNN, LSTM, CNN-BiLSTM, and Attention-CNN-BiLSTM, our approach surpasses them in terms of effectiveness, accuracy, and practicability. Conclusions: We hope that this comprehensive activity recognition system may be augmented with an advanced disability monitoring and diagnosis system to facilitate predictive assistance and personalized rehabilitation strategies.