Deep-Learning-for-Human-Activity-Recognition and har-keras-cnn
These two tools are competitors, as both offer Keras-based implementations of convolutional neural networks for human activity recognition, with tool A providing a broader range of deep learning models and LightGBM in addition to CNNs, while tool B focuses specifically on 1D CNNs.
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.
About har-keras-cnn
ni79ls/har-keras-cnn
Jupyter Notebook for Human Activity Recognition (HAR) with 1D Convolutional Neural Network in Python and Keras
This helps sports scientists, fitness researchers, or anyone analyzing human movement automatically identify different activities from sensor data. You input raw accelerometer data, typically from wearable devices, and it outputs predictions classifying movements like walking, running, or jogging. It's designed for practitioners who need to categorize physical activities based on time-series sensor readings.
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