Human-Activity-Recognition-using-CNN and Deep-Learning-for-Human-Activity-Recognition
About Human-Activity-Recognition-using-CNN
aqibsaeed/Human-Activity-Recognition-using-CNN
Convolutional Neural Network for Human Activity Recognition in Tensorflow
This project helps you classify everyday physical actions, like walking, jogging, or sitting, from sensor data. It takes raw data from smartphone accelerometers and gyroscopes and tells you exactly what activity a person was performing. This tool is useful for researchers or product developers working with wearable technology or health monitoring applications.
About Deep-Learning-for-Human-Activity-Recognition
takumiw/Deep-Learning-for-Human-Activity-Recognition
Keras implementation of CNN, DeepConvLSTM, and SDAE and LightGBM for sensor-based Human Activity Recognition (HAR).
This project helps classify human activities like walking, standing, or sitting using data from smartphone sensors. It takes raw accelerometer and gyroscope readings and outputs a prediction of the activity being performed. This is useful for researchers or developers creating applications that need to understand user movement and behavior.
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