fall-detection-and-predction-using-GRU-and-LSTM-with-Transfer-Learning
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20 hours ago •
IKKIM00
IKKIM00
Fall Detection and Prediction using GRU and LSTM with Transfer Learning
SmartFall Dataset Description
- all fall data samples are equally 25 points
- the cycle repeats with 'ADL-FALL-ADL-FALL'
SmartFall LSTM & GRU Classification
- To make input dataset, I have partioned data into 40 points
- If all 25 points of fall data is included in partioned 40 points, I labelled it as 'FALL'
- Unless, it's all labelled as 'ADL'
- Since there is huge data imbalance between Fall and ADL, I shrinked ADL data portion as same as Fall data
- I also implemented cyclic learning rate
SmartFall LSTM & GRU Classification Results
| Precision | Recall | F1 Score | Accuracy | |
|---|---|---|---|---|
| SmartFall LSTM | 0.9963 | 0.8411 | 0.9121 | 0.9189 |
| SmartFall GRU | 0.9963 | 0.8442 | 0.9139 | 0.9205 |
MobiAct Dataset
- To compare the result between MobiAct dataset and SmartFall dataset, I resampled MobiAct dataset similar to SmartFall dataset
- To make input data, I have resampled all MobiAct Fall data's fall parts to 30 points and added 10 samples of ADL at the front and the end of fall samples
- Then I used partioned data with window size of 40(same as SmartFall data window size) to make input dataset
- You can find MobiAct Dataset at below url
- https://bmi.hmu.gr/the-mobifall-and-mobiact-datasets-2/
- You can see partioning code throught MobiAct_DataParsing.ipynb
Transfer Learning using MobiAct Dataset
- I have used pretrained model using SmartFall Dataset to MobiAct Dataset but because of the difference of data collected domain performance was really bad
- The chart below describe the result of training SmartFall data & testing on SmartFall data, using pretrained model to test on MobiAct data, relearning pretrained model using MobiAct data
- The chart shows that relearning through pretrained model works well
IKKIM00