Integrating swin transfer with attention mechanism based hybrid deep learning driven automated human activity recognition for enhanced disability assistance

The challenge of providing independent living for elderly and disabled individuals is a critical societal concern. Accurate human activity recognition (HAR) is core to allow the development of context-aware applications that involve the identification and understanding of human behaviour, for example, monitoring elderly or disabled people who live alone. HAR performed using ambient sensors, such as cameras or wearable devices, has gained prominence due to its wide-ranging applications in healthcare, surveillance, smart environments, and security. However, choosing an appropriate AI model for precisely interpreting intrinsic human activities remains a key challenge in the field. And, it is beneficial for people with disabilities or the elderly to live independently. Currently, the methods of artificial intelligence (AI) for activity recognition, an optimal application area, and the form of data acquisition devices make the selections more complex. Different researchers applied deep learning (DL) techniques in HAR. At present, DL has achieved remarkable results in developing high-level ideas from composite data in various fields like HAR. In this study, a Hybrid Deep Learning with an Attention Mechanism for Automatic Human Activity Recognition Using Swin Transformer (HDLAM-AHARST) model is proposed. The aim is to design an intelligent HAR system to assist individuals with disabilities by enabling accurate and real-time monitoring for improved quality of life. Initially, the Gabor filter (GF) method is utilized in the image pre-processing step to eliminate noise and enhance image quality. Furthermore, the Swin Transformer (SwinT) method is utilized for the feature extraction process to identify and transform relevant information from data. Moreover, the hybridization of a convolutional neural network and a long short-term memory with an attention mechanism (C-LSTM-A) is employed for the HAR classification process. Finally, the hyperparameter selection for the C-LSTM-A model is performed by using the Lyrebird Optimisation Algorithm (LOA) method. The experimentation of the HDLAM-AHARST technique is performed under the HAR image dataset. The comparison study of the HDLAM-AHARST technique illustrated an accuracy rate of 98.91% over existing methods.