An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection

TL;DR

This paper presents PoseTrack, a mobile app utilizing Raspberry Pi and MediaPipe to monitor and correct sitting posture in real-time, aiming to reduce posture-related health issues.

Contribution

The study introduces a novel system combining Raspberry Pi, MediaPipe, and mobile app technology for real-time posture monitoring and feedback.

Findings

System effectively detects poor posture in real-time

High accuracy when joints are visible and unblocked

Potential for large-scale posture monitoring

Abstract

Poor posture has become an increasingly prevalent concern due to students and workers spending extended amounts of time sitting at a desk. To address this issue, we developed PoseTrack, a mobile application that uses a Raspberry Pi Camera and Mediapipe Pose landmarks to monitor the user\'s posture and provide real time feedback. The system detects poor posture, including forward lean, slouching, hunched shoulders, crossed legs, etc. Some challenges we faced were obtaining posture data, transferring data from the Raspberry Pi to the App, and safely storing user data. We used a Flask server to pass data from the Raspberry Pi to the mobile application, Firebase to store user data, and the Flutter framework to create the app. To test the analysis system viability, we designed an experiment that tested the system accuracy across several different perspectives and postures. The results indicate that the system is able to effectively detect poor posture whenever the user\'s joints are not blocked by the table or their limbs. The results demonstrate the potential for the system to be further improved and used on a larger scale for poor posture monitoring.