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For HAR, our novel HDAD (IGAV) dataset was constructed by performing 4 dynamic and 3 static activities with the accelerometer and gyroscope sensors of the IOS smart phone in two different positions for a total of 15 seconds. Mentioned activities were collected in real time by placing them on the waist of a total of 10 volunteers.
"Embark on a cutting-edge journey in Human Activity Recognition using a fusion of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This project includes model training, metric visualization, and action prediction in videos. Experience seamless interaction with a Streamlit-powered user-friendly version (at the bottom)