Jayesh368/Human-Activity-Recognition
This Project is based on the human activity detection. In this project we have collected the data of 12 activities from the sensors of the smartphone using Andro-Sensor mobile application then we have perform EDA and performed required statistical calculation and added it in our training dataset in feature engineering. We have visualise the high demensional data using t-SNE and implemented machine learning classifier SVM using poly and rbf kernel, KNN, Logistic Regression, Decision Tree , XGBoost then applied GridSearch to get best parameters and we got best accuracy in Logistic Regression and we have successfully tested new test data in model. This model can be used for health monitoring activities pattern.
This helps health monitoring professionals or researchers automatically identify daily activities like walking, sitting, or running from smartphone sensor data. It takes raw movement data from phone sensors and classifies it into one of 12 common human activities. The output is a categorized log of activities, useful for understanding activity patterns of individuals.
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Use this if you need to automatically detect and log a person's physical activities using data collected from their smartphone.
Not ideal if you need to detect highly specialized activities not covered by the 12 pre-defined categories or require real-time, low-latency activity detection on a device itself.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Sep 30, 2021
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