1. Bench press exercise detection and repetition counting
Martin Barus

2. Motivation Track more than what you did, track how you did it.
Track exercising
Boost performance
Analyze progress

3. How is it done wearable device (accelerometers and gyros)
mobile application
server application

4. Real tasks Distinguish exercise from other movements
100 minutes of labeled data (25 minutes of bench press exercising, 75 minutes of nonexercise) recorded at 50 Hz
Classify each 5 second window with 225 extracted features as exercise/non- exercise (SVM, NB, ANN, clustering)
Identify repetitions
How many repetitions were made, when
PCA to make 3D signal 1D, percentile thresholding into peak middle valley, count the transitions

5. Why is it hard

6. What has been done so far
Distinguish exercise from other movements
Accuracy 92 % for polynomial SVM,
Accuracy 88 % for Naive Bayes with PCA
Accuracy 80% using clustering with PCA
Identify repetitions
RMSE 1.90, ME 1.02 for ground truth
RMSE 7.07, ME 5.26 if considering all data
RMSE 2.12, ME 1.35 for SVM
RMSE 2.73, ME 1.82 for Naive Bayes with PCA
RMSE 4.42, ME 3.26 for clustering

7. Related work
us/um/redmond/groups/cue/publications/Morris_Workout_CHI_2014.pdf