Human Activity Recognition

With the expansion of smart homes, Human Activity Recognition (HAR) has become a key challenge in artificial intelligence, enhancing not only the comfort and safety of residents but also contributing to the development of applications such as healthcare and smart surveillance. The Transformer architecture, with its ability to model long-term dependencies and process data in parallel, has made significant advancements in recognizing human activities. In addition, its multi-head attention mechanism enables the analysis of complex input data by allowing the model to focus on different parts of the input simultaneously, capturing diverse relationships and dependencies within the data. This paper examines the application of Transformers in HAR and analyzes recent studies (since 2019). In addition to investigating innovative architectures, feature extraction methods, and accuracy improvements, it also discusses the challenges and future prospects of these models in recognizing human activities. Rapid advancements in deep learning and access to extensive datasets have made Transformers a key tool for improving the accuracy and efficiency of HAR systems in smart environments.

The Internet of Things (IoT) has become increasingly prevalent, and recent advances in machine learning, particularly in healthcare, have gained significant attention from researchers. One prominent interdisciplinary topic in these fields is human activity recognition (HAR). Despite extensive research, several challenges remain in this area, especially concerning the application of modern machine learning techniques for HAR. This study proposes a novel method for human activity recognition by combining radial basis function neural networks (RBFNN) and support vector machines (SVM). The approach enhances recognition accuracy and algorithm efficiency by extracting relevant features using RBFNN and convolutional neural networks (CNN). Classification is then performed using SVM. The proposed method was evaluated using the UCI HAR dataset, which includes six distinct human activities. Results demonstrate that the proposed approach achieves an accuracy of 99%, surpassing existing methods.