Human Motion Recognition with Smartphone Data

The Human Motion (Activity) Recognition (HAR) is significant as it can provide valuable insights into human activity patterns and behaviors, enabling the development of personalized and context-aware healthcare solutions. The use of smartphones in The Human Activity Recognition projects can facilitate the collection of real-world data over extended periods, leading to more ecologically valid and reliable activity recognition. This project proposes a Human Activity Recognition system that leverages Random Forests and Neural Networks to enhance accuracy in wearable and mobile technology. Utilizing the UHAR dataset from Kaggle, which includes sensor data for six activities— walking, climbing stairs, sitting, standing, and laying down—the system can accurately classify these activities. The benefits of this project include promoting health and fitness, assessing fall risks, encouraging ergonomic improvements, and analyzing sleep quality, thereby providing valuable insights into various aspects of health and well-being. By using Human Activity Recognition using smartphones with neural networks and random forest algorithm project can be valuable in various healthcare applications like monitoring patient activity levels, rehabilitation programs, and fall detection for elderly caring people.