LSTM-Human-Activity-Recognition and Human-Activity-Recognition-using-CNN
These are competitors, as both repositories provide different deep learning model implementations—LSTM vs. CNN—for the same task of human activity recognition.
About LSTM-Human-Activity-Recognition
guillaume-chevalier/LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
This project helps anyone working with sensor data from smartphones to automatically identify six common human activities: walking, walking upstairs, walking downstairs, sitting, standing, and laying. It takes raw accelerometer and gyroscope data as input and outputs a classification of the activity being performed. This is useful for researchers, product developers, or data analysts in fields like health, fitness, or behavioral science.
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.
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