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Predicting Human Activity using LSTM Tensorflow and Keras RNN models

License: MIT License

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human-activity-recognition's Introduction

Human Activity Recognition

INFO 7245- Big-Data-Systems-and-Intelligence-Analytics
Final Project

Problem Statement: Predict Human Activity and classify them into WALKING, WALKING UPSTAIRS, WALKING DOWNSTAIRS, SITTING, STANDING and LAYING

Dataset: Human Activity Recognition
7352 observations with 128 datapoints taken within 2.56 seconds with 50% carry forwarded in the training dataset. Each datapoint has 9 measurements for:
• Body Acceleration X axis
• Body Acceleration Y axis
• Body Acceleration Z axis
• Body Gyroscope X axis
• Body Gyroscope Y axis
• Body Gyroscope Z axis
• Total Acceleration X axis
• Total Acceleration Y axis
• Total Acceleration Z axis

Goal:
• Showcase that Standard Neural Networks shouldn’t be used to predict Sequential Classification Problem
• Implement Deep Neural Networks and prove it to be better than Standard Neural Network for Sequential Classification Problem

Steps Completed:
• Importing data
• Exploratory Data Analysis
• Implementing Machine Learning algorithms
>o Decision Tree
>o K Nearest Neighbors
>o SVC
>o Gaussian Naïve Bayes
>o Quadratic Discriminant Analysis
• Implementing Recurrent Neural Network (RNN) with keras
• Implementing Long-Short Term Memory (LSTM) with tensorflow
• Created Android App to track Human Activity using Accelerometer and Gyroscope sensors

Results:
LSTM has a better accuracy at predicting the human activity as compared to any machine learning algotihm or even RNN. RNN has 81.81% accuracy where as LSTM has 88.97% accuracy

Future Scope:
• Inclusion of third variable to improve prediction accuracy
• Android App enhancements with user better interface

For more details, please refer HumanActivityRecognition_Report

Project Website: https://krutsdeshpande.wixsite.com/bigdata-har

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02akshay / human-activity-recognition Goto Github PK