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An Artificial Intelligence Exercise Coaching Mobile App: Development and Randomized Controlled Trial to Verify Its

Han Joo Chae¹, Ji-Been Kim², Gwanmo Park¹

¹Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

Interactive Journal of Medical Research

|September 12, 2023

Summary

A new mobile app uses deep learning to provide real-time feedback on squat form, significantly improving posture and knee joint angles for users compared to traditional exercise videos.

Keywords:

COVID-19 deep-learning exercise home workout mobile assistant mobile device physical activity posture correction social distance workout

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Area of Science:

Biomedical Engineering
Computer Science
Sports Science

Background:

Physical inactivity increases disease risk; incorrect exercise form can lead to injury.
Existing remote coaching and posture correction solutions are often expensive or inefficient.
Mobile devices offer a potential platform for accessible, AI-powered fitness guidance.

Purpose of the Study:

To develop a mobile application using deep neural networks for personalized squat posture feedback.
To leverage deep learning to mimic expert assessment of exercise form.
To evaluate the app's effectiveness in improving squat technique compared to standard exercise videos.

Main Methods:

Trained a deep learning model on over 20,000 expert-annotated squat videos using pose estimation and video classification.
Developed the 'Home Alone Exercise' mobile app for real-time posture analysis.
Conducted a 2-week randomized controlled trial comparing app users (EXP) with a control group (CTL) using only exercise videos.

Main Results:

The EXP group showed significant improvements in squat posture scores (P=.001) and knee joint angles (P=.02-.03) after 2 weeks.
The CTL group exhibited no significant changes in squat posture or knee joint angles.
The app effectively identified and guided users toward correct squat execution.

Conclusions:

The mobile workout assistant provides cost-effective, self-guided feedback for squat exercises.
Users trained with the app demonstrated faster learning and better understanding of nuanced exercise details.
This AI-powered approach offers an efficient alternative to in-person training for improving exercise form.