Human Activity Recognition Using Tensorflow
N S Nisarga · International Journal for Research in Applied Science and Engineering Technology · 2024
Human Activity Recognition (HAR) has become a key focus in healthcare and machine learning, aiming to improve personal well-being and lifestyle management through sensor data. As individuals lead increasingly busy lives, continuous monitoring of their activities can provide valuable insights for health management. Despite advancements, identifying patterns in human activity remains a challenging task, especially with diverse sensor data sources. This paper explores various technical approaches for human activity recognition, focusing on the use of TensorFlow and Long Short-Term Memory (LSTM) networks, which are effective in modelling time-series data from devices like smartphones and wearables. The paper highlights the potential of these models for real-time activity classification