An implementation of the paper "Skeleton-based abnormal gait detection" (Sensors, MDPI 2016)
- Python
- Numpy
- Scipy
- Scikit-learn
- hmmlearn
- Matplotlib
- The code was implemented to directly work on DIRO gait dataset
- Please download the skeleton data and put the npz file into the folder dataset
python main.py -l 0 -w 5 -s 24 -o 43 -f 0
- -l: use leave-one-out cross-validation (boolean)
- -w: width of smoothing window (int)
- -s: number of HMM's states (int)
- -o: number of HMM's observations (int)
- -f: write results to file (boolean)
Default training and test sets
test subject(s): [1 3 6 7]
Load normal gaits of 5 subjects for training...
processing normal skel. of subject 0
processing normal skel. of subject 2
processing normal skel. of subject 4
processing normal skel. of subject 5
processing normal skel. of subject 8
Load test data...
processing skel. of subject 1
processing skel. of subject 3
processing skel. of subject 6
processing skel. of subject 7
window width = 5, states = 24, observations = 43
kmeans dimension: 7
TEST RESULTS
Full sequence: AUC = 0.898 --- EER = 0.250
Cycle: AUC = 0.792 --- EER = 0.277
Leave-one-out cross-validation
...
Leave-one-out means
Full sequence: AUC = 0.806 --- EER = 0.173
Cycle: AUC = 0.607 --- EER = 0.417