LSTM-Human-Activity-Recognition and Deep-Learning-for-Human-Activity-Recognition
Both projects offer distinct Keras-based deep learning implementations for human activity recognition, making them **competitors** where one would likely choose one over the other based on the specific architectural preferences (LSTM vs. CNN, DeepConvLSTM, SDAE with LightGBM) or the dataset used in the examples.
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 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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