Bench press exercise detection and repetition counting Martin Barus

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

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

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

Why is it hard

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

Related work http://research.microsoft.com/en- us/um/redmond/groups/cue/publications/Morris_Workout_CHI_2014.pdf http://www.cs.berkeley.edu/~kenghao/publications/freeweight_ubicomp2007.PDF