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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.

Home Page: https://doi.org/10.17341/gazimmfd.772849

License: MIT License

Python 100.00%
activity-recognition bi-directional cnn deep-learning gru har human-activity-recognition humanactivityrecognition keras keras-neural-networks keras-tensorflow lstm lstm-neural-networks machine-learning neural-network recurrent-neural-networks sensor tensorflow

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