Android Human Activity Recognition Tensorflow Project

This is the source code for a sensor-based Android Human Activity Recognition App. The model has been built with Keras deep learning library. The classifier has been trained and validated on \"Sensors Activity Dataset\" by Shoaib et al. which is available for download from
here
. The dataset contains data for seven activities of daily living including biking, downstairs, jogging, sitting, standing, upstairs, and walking. An
LSTM
learner has been employed for classification task which achieved an accuracy of
98%
on valdiation data. Finally the model has been exported in
protobuf
format to be used in android app for
on-device inference
. You can check out the jupyter notebook that goes along to follow all the steps which have been taken to build and export the model.
Dependencies
Python 3.6
Tensorflow 1.13.1
Keras
Android Requirements :
Android Studio Latest.
Grandle version 4.2.2, 7.0.2
Emulator
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Detailed Project Description

Android Human Activity Recognition Tensorflow Project applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- Android
- TensorFlow
- Deep Learning

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Android, TensorFlow) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface