Smart Wearables Powered by AI Transforming Human Activity Recognition

Abstract

Smart clothing has changed the ways that human behaviour is observed and analyzed, finding its uses in health and fitness, and assisting in daily living. Nevertheless, conventional techniques used in HAR are mostly based on feature extraction by designers and the use of fixed algorithms that cannot address the dynamic aspects of human activities. HAR can be advanced through devices supported by artificial intelligence, and this research seeks to investigate how wearable technologies can improve this field of study. Hence, using CNN and RNN deep learning architectures this study constructs a comprehensive model with the potential of detecting various human activities instantaneously and accurately. The framework includes the use of sensor fusion approaches to process data collected from accelerometers, gyroscopes and heart rate sensors, to fully capture physical movements. Specifically, to high performance and efficiency in the computations of the model, several preprocessing and feature extraction techniques are employed. Outcome analysis shows that the proposed AI-based framework recognizes a subject’s identity with more than 95% accuracy across the different datasets comping basic machine learning techniques. The versatility of the system demonstrated in this work regarding wearable platforms and activities suggests that the proposed solution could be useful for practical application in fitness, health care, and rehabilitation. The findings of this work point to critical opportunities that are driven by AI for future wearable technology; a more adept and integrated system that can preserve the health and efficiency of humans. The results pave the way for further advancements in using AI to support HAR with a focus on scalability, real-time applicability, and inclusiveness.