Android-based Indoor Physical Activities Detection and Monitoring System using Pose Estimation

Open Access
Conference Proceedings
Authors: Imran Ullah KhanSana TahirJong Weon Lee

Abstract: In the last decade, innovation in the fitness and health industry has exponentially expanded, resulting in new devices like fitness trackers, calorie counters, diet planners, and running trackers. However, a large proportion of the population is elder people who need technical assistance to live their life healthy and independently. In order to progress the advancement in this research area, we concentrated on developing a mobile application that can recognize and assist human actions during exercise and store their records. In this study, a computer vision-based application is developed that can recognize and monitor indoor physical exercises of people especially elders through a smartphone camera. The application uses the pose estimation method to recognize the exercises and further counts the number of exercises and repetitions. This application can recognize 12 different physical exercises, which are further divided into three main categories such as upper body (dumbbell press, overhead arms, back twist, chest, shoulder, front arm), lower body (chair stand, leg raise forward/backward, side leg raise, squats, balance walk), and whole-body (pushups) exercises. An android application is developed which combines OpenPose, deep learning framework, and OpenCV for pose estimation, learning, and visualization while the android studio is used for application development. We use the Media pipe library for the detection and tracking of 33 different points in the human body, including face points. For pose estimation and activity recognition, we calculate different angles between body-detected joints, which are used to recognize the exercise. Moreover, we also measure how many repetitions the user performs in a specific exercise. Finally, the application was tested on 15 different users, which gave positive feedback, and the results were quite accurate for detecting and counting the exercises. Furthermore, the application is very useful for elderly people who mostly stay in their homes or people who can’t join the Gym outside due to their busy schedules.

Keywords: Deep learning, Pose Estimation, HCI, Android, Physical Activities Detection.

DOI: 10.54941/ahfe1003671

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