aqibsaeed/Human-Activity-Recognition-using-CNN
A Convolutional Neural Network implemented in TensorFlow to classify human activities (walking, sitting, standing, etc.) from accelerometer sensor data.
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This repository contains a Jupyter notebook that implements a CNN-based model for Human Activity Recognition. It processes time-series sensor data from the WISDM Actitracker dataset to classify six activities. The model uses TensorFlow to build, train, and evaluate a convolutional neural network architecture on accelerometer readings.
Frequently asked
- What is aqibsaeed/Human-Activity-Recognition-using-CNN?
- A Convolutional Neural Network implemented in TensorFlow to classify human activities (walking, sitting, standing, etc.) from accelerometer sensor data.
- Is Human-Activity-Recognition-using-CNN open source?
- Yes — aqibsaeed/Human-Activity-Recognition-using-CNN is open source, released under the Apache-2.0 license.
- What language is Human-Activity-Recognition-using-CNN written in?
- aqibsaeed/Human-Activity-Recognition-using-CNN is primarily written in Jupyter Notebook.
- How popular is Human-Activity-Recognition-using-CNN?
- aqibsaeed/Human-Activity-Recognition-using-CNN has 485 stars on GitHub.
- Where can I find Human-Activity-Recognition-using-CNN?
- aqibsaeed/Human-Activity-Recognition-using-CNN is on GitHub at https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN.