An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916902075367424 |
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| author | Yung-Chen Hsieh Sun, Yu |
| author_facet | Yung-Chen Hsieh Sun, Yu |
| contents | 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 userś 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 userś 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_11683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection Yung-Chen Hsieh Sun, Yu Computers and Society 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 userś 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 userś 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. |
| title | An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2508.11683 |