Human Activity Recognition for Enhanced Healthcare Monitoring Using Deep Learning

Human Activity Recognition (HAR) is increasingly important due to its potential to transform healthcare by enabling proactive and continuous monitoring of patient activities, facilitating early detection of health risks, and enhancing personalized care. Real-time recognition of human activities can significantly contribute to improved health outcomes by providing critical insights into patient behavior, mobility patterns, and rehabilitation progress.We utilized sensor data collected from hip-mounted accelerometers and gyroscopes provided in the USC-HAD dataset. While our approach focuses on hip-worn data, the resulting models may provide insights for similar deployments in wearable healthcare technologies. Utilizing the USC-HAD dataset, the proposed method achieves high classification accuracy while addressing challenges related to real-world data variability and class imbalance. Experimental results demonstrate the system’s effectiveness in accurately identifying a broad spectrum of activities, crucial for clinical and wellness monitoring scenarios. We discuss how HAR can augment remote patient care, rehabilitation, and personalized health services by enabling real-time tracking of daily activities and movement patterns. The paper concludes with an in-depth consideration of current limitations, potential integration into healthcare infrastructures, and future research directions to further enhance reliability, scalability, and patient-centered applications. The proposed HAR system achieves a high classification accuracy of approximately 98% using advanced LSTM networks, demonstrating its potential effectiveness in clinical and wellness monitoring scenarios.