Related papers: P\=uioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System

P\=uioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System

URL: http://arxiv.org/abs/2308.02420v1
Date: Sat, 22 Jul 2023 01:38:02 GMT
Title: P\=uioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System
Authors: Adam Sinclair, Kayla Kautai, and Seyed Reza Shahamiri
Abstract summary: 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.
Score: 1.4050836886292868
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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