Measuring the Performance of Push-ups - Qualitative Sport Activity Recognition
The trend of mobile activity monitoring using widely available technology is one of the most blooming concepts in the recent years. It supports many novel applications, such as fitness games or health monitoring. In these scenarios, activity recognition tries to distinguish between different types of activities. However, only little work has focused on qualitative recognition so far: How exactly is the activity carried out? In this paper, an approach for supervising activities, i.e. qualitative recognition, is proposed. The focus lied on push-ups as a proof of concept, for which sensor data of smartphones and smartwatches were collected. A user-dependent dataset with 4 participants and a user-independent dataset with 16 participants were created. The performance of Naive Bayes classifier was tested against normal, kernel and multivariate multinomial probability distributions. An accuracy of 90.5% was achieved on the user-dependent model, whereas the user-independent model scored with an accuracy of 80.3%.