  Automated exercise repetition counting has applications across the physical
fitness realm, from personal health to rehabilitation. Motivated by the
ubiquity of mobile phones and the benefits of tracking physical activity, this
study explored the feasibility of counting exercise repetitions in real-time,
using only on-device inference, on smartphones. In this work, after providing
an extensive overview of the state-of-the-art automatic exercise repetition
counting methods, we introduce a deep learning based exercise repetition
counting system for smartphones consisting of five components: (1) Pose
estimation, (2) Thresholding, (3) Optical flow, (4) State machine, and (5)
Counter. The system is then implemented via a cross-platform mobile application
named P\=uioio that uses only the smartphone camera to track repetitions in
real time for three standard exercises: Squats, Push-ups, and Pull-ups. The
proposed system was evaluated via a dataset of pre-recorded videos of
individuals exercising as well as testing by subjects exercising in real time.
Evaluation results indicated the system was 98.89% accurate in real-world tests
and up to 98.85% when evaluated via the pre-recorded dataset. This makes it an
effective, low-cost, and convenient alternative to existing solutions since the
proposed system has minimal hardware requirements without requiring any
wearable or specific sensors or network connectivity.
