Posture detection for physical therapy application
The recent Covid-19 pandemic has greatly affected the way people interact and has caused widespread social disruptions. A survey conducted by the world physiotherapy member organization showed that over 70% of physiotherapy services were impacted by the pandemic, primarily due to the harsh lockdowns...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166826 |
| _version_ | 1824457006634762240 |
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| author | Yeo, Nigel |
| author2 | Pui Tze Sian |
| author_facet | Pui Tze Sian Yeo, Nigel |
| author_sort | Yeo, Nigel |
| collection | NTU |
| description | The recent Covid-19 pandemic has greatly affected the way people interact and has caused widespread social disruptions. A survey conducted by the world physiotherapy member organization showed that over 70% of physiotherapy services were impacted by the pandemic, primarily due to the harsh lockdowns imposed by many countries and the fear of patients contracting the virus. This has led to a shortage of physiotherapists and has made it difficult for many patients to access timely and consistent physiotherapy treatment. To address these challenges, there is a need for a cost-effective and remote physiotherapy solution for patients who are unable to access in-person treatment. This project aimed to develop such a solution by creating a real-time posture detector using the Body Detection package for a physiotherapy mobile application. Data was collected and used to optimize a pre-trained machine learning model which was utilized to classify poses and developed pose estimation algorithms for physiotherapy applications. The application was developed using Flutter SDK and Dart programming language and leverages the Body Detection package to detect the spatial locations of key body points as well as their geometrical angles. The development of pose estimation algorithms represents a major shift in the assessment and analysis of human movement. By utilizing advanced computer vision, these algorithms can track human motion in real-time through video footage captured by common, low-cost devices such as smartphones, tablets, and laptops. This makes it possible to provide remote and affordable physiotherapy treatment to patients who are unable to access in-person treatment. The results of the tests conducted on the application demonstrate its effectiveness in accurately detecting the posture of the user. This project has successfully developed a solution that can provide remote and affordable physiotherapy treatment and has the potential to positively impact the provision of physiotherapy services. |
| first_indexed | 2025-02-19T04:03:08Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166826 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T04:03:08Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1668262023-05-19T15:32:07Z Posture detection for physical therapy application Yeo, Nigel Pui Tze Sian School of Chemical and Biomedical Engineering tspui@ntu.edu.sg Engineering::Bioengineering The recent Covid-19 pandemic has greatly affected the way people interact and has caused widespread social disruptions. A survey conducted by the world physiotherapy member organization showed that over 70% of physiotherapy services were impacted by the pandemic, primarily due to the harsh lockdowns imposed by many countries and the fear of patients contracting the virus. This has led to a shortage of physiotherapists and has made it difficult for many patients to access timely and consistent physiotherapy treatment. To address these challenges, there is a need for a cost-effective and remote physiotherapy solution for patients who are unable to access in-person treatment. This project aimed to develop such a solution by creating a real-time posture detector using the Body Detection package for a physiotherapy mobile application. Data was collected and used to optimize a pre-trained machine learning model which was utilized to classify poses and developed pose estimation algorithms for physiotherapy applications. The application was developed using Flutter SDK and Dart programming language and leverages the Body Detection package to detect the spatial locations of key body points as well as their geometrical angles. The development of pose estimation algorithms represents a major shift in the assessment and analysis of human movement. By utilizing advanced computer vision, these algorithms can track human motion in real-time through video footage captured by common, low-cost devices such as smartphones, tablets, and laptops. This makes it possible to provide remote and affordable physiotherapy treatment to patients who are unable to access in-person treatment. The results of the tests conducted on the application demonstrate its effectiveness in accurately detecting the posture of the user. This project has successfully developed a solution that can provide remote and affordable physiotherapy treatment and has the potential to positively impact the provision of physiotherapy services. Bachelor of Engineering (Bioengineering) 2023-05-15T00:54:42Z 2023-05-15T00:54:42Z 2023 Final Year Project (FYP) Yeo, N. (2023). Posture detection for physical therapy application. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166826 https://hdl.handle.net/10356/166826 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Bioengineering Yeo, Nigel Posture detection for physical therapy application |
| title | Posture detection for physical therapy application |
| title_full | Posture detection for physical therapy application |
| title_fullStr | Posture detection for physical therapy application |
| title_full_unstemmed | Posture detection for physical therapy application |
| title_short | Posture detection for physical therapy application |
| title_sort | posture detection for physical therapy application |
| topic | Engineering::Bioengineering |
| url | https://hdl.handle.net/10356/166826 |
| work_keys_str_mv | AT yeonigel posturedetectionforphysicaltherapyapplication |