Real-time Fall Detection for RNN(AFD-RNN)

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

AFD-RNN using RNN

The sensors(acceleration and gyroscope sensor) is realtime to collect data,so we using rnn to detect the people movement.

Requirenment

• TensorFlow >= 1.4
• python3
• matplotlib

Class

Sitting,standing,stand to sit,sit to stand,upstairs,downstairs,lying,jumping,joging,walking and fall.

Train and test

1.Train data

• The data collect frequence is 50Hz
• Need acceleration and gyroscope sensor

2.Before training

Put the train data to ./dataset/train/,and use kalman filter to handle the data.

python utils.py

3.Training

python train_rnn.py

4.Testing

Put the test data to ./dataset/test/,and use kalman filter to handle the data.

python run_rnn.py

Dataset

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%