| Issues | Our Solution |
|---|---|
| Less relevance of ads | Smart health assistant - Improvement of recommendations based on user’s activity |
| Extra hardware to detect person’s activity | Improvements of ads by categorizing ads for different physical activities |
| No generic API/SDK for the above | API/SDK for other developers |
- Step 1 - Data generation
- Step 2 - Training our activity prediction model
- Step 3 - Using prediction model on smartphones (offline)
- Android application developed to generate data
- Collected data of each activity for 10-15 mins from seven android phones.
- Use of generated data to train our prediction model
- Using Convolutional Neural Network (Machine Learning concept)
- Combined 100 samples (at 50 Hz) of accelerometer data for training
- Performed 1D Convolutional with 60x3 and 20x1 weight matrix, followed by Max Pooling
- Achieved accuracy of more than 90%
- Import trained model to phone to predict the activity offline
- Used NDK* provided by android to link TensorFlow to JAVA
- Predict activity for 100 readings
- Taking 100 predictions (10/minute) and finding the most occurred activity
- Build Notifications by fetching the data from local database
*NDK - Native Development Kit
