result picture illustrate：
- The red,green,blue lines is acceleration sensor's x,y,z data。
- In the picture ,"correct" is the ground truth,"predict" is AFD-RNN network predict data
- Fall1、Fall2、Fall3 and Fall4 are represent Forward-lying,Front-knees-lying,Back-sitting-chair,Sideward-lying
The sensors(acceleration and gyroscope sensor) is realtime to collect data,so we using rnn to detect the people movement.
- TensorFlow >= 1.4
- python3
- matplotlib
Sitting,standing,stand to sit,sit to stand,upstairs,downstairs,lying,jumping,joging,walking and fall.
- The data collect frequence is 50Hz
- Need acceleration and gyroscope sensor
Put the train data to ./dataset/train/,and use kalman filter to handle the data.
python utils.py
python train_rnn.py
Put the test data to ./dataset/test/,and use kalman filter to handle the data.
python run_rnn.py
We using public dataset MobileFall to train and test our net.
I upload the dataset at Baidu网盘,if you cant download from MobileFall,you can try this
The final accuracy is 98.78%
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