Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT
Addressing the widespread problem of poor posture and its far-reaching health implications, our innovative solution employs advanced interdisciplinary approaches and IoT technology for real-time bad posture detection. By integrating smart sensors and wearables strategically placed to monitor body po...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/37/e3sconf_icftest2024_01045.pdf |
| _version_ | 1827292568037097472 |
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| author | Sreevani V. Sathwik Reddy B. Nithin K. Harsha Vardhan K. Mohammed Kahtan A. Reddy Uma Lakhanpal Sorabh Kalra Ravi |
| author_facet | Sreevani V. Sathwik Reddy B. Nithin K. Harsha Vardhan K. Mohammed Kahtan A. Reddy Uma Lakhanpal Sorabh Kalra Ravi |
| author_sort | Sreevani V. |
| collection | DOAJ |
| description | Addressing the widespread problem of poor posture and its far-reaching health implications, our innovative solution employs advanced interdisciplinary approaches and IoT technology for real-time bad posture detection. By integrating smart sensors and wearables strategically placed to monitor body positioning continuously, our system goes beyond conventional methods. The gathered posture data undergoes analysis by processing units, incorporating advanced algorithms that draw insights from fields like biomechanics and human-computer interaction. This holistic approach not only identifies instances of poor posture with heightened accuracy but also provides immediate feedback to users through visual cues or notifications, fostering self-awareness and encouraging posture correction. The versatility and scalability of our solution make it applicable to diverse settings, including offices, healthcare, and education. This paper delves into the design, implementation, and challenges of our IoT-based system, emphasizing its potential to mitigate health risks linked to prolonged poor posture. By embracing advanced interdisciplinary approaches, we contribute to a more comprehensive understanding of posture-related complexities, paving the way for future advancements in public health through the promotion of better posture habits. |
| first_indexed | 2024-04-24T13:06:47Z |
| format | Article |
| id | doaj.art-e14f1b2984104e2ca60e1bb1465181dc |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-24T13:06:47Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-e14f1b2984104e2ca60e1bb1465181dc2024-04-05T07:29:48ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015070104510.1051/e3sconf/202450701045e3sconf_icftest2024_01045Advanced interdisciplinary approaches for bad posture detection using computer vision and IoTSreevani V.0Sathwik Reddy B.1Nithin K.2Harsha Vardhan K.3Mohammed Kahtan A.4Reddy Uma5Lakhanpal Sorabh6Kalra Ravi7Department of AIMLE, GRIETDepartment of AIMLE, GRIETDepartment of AIMLE, GRIETDepartment of AIMLE, GRIETDepartment of medical physics, college of medical sciences, Jabir Ibn Hayyan medical universityDepartment of Artificial Intelligence and Machine Learning, New Horizon College of EngineeringLovely Professional UniversityLloyd Institute of Engineering & Technology, Knowledge Park IIAddressing the widespread problem of poor posture and its far-reaching health implications, our innovative solution employs advanced interdisciplinary approaches and IoT technology for real-time bad posture detection. By integrating smart sensors and wearables strategically placed to monitor body positioning continuously, our system goes beyond conventional methods. The gathered posture data undergoes analysis by processing units, incorporating advanced algorithms that draw insights from fields like biomechanics and human-computer interaction. This holistic approach not only identifies instances of poor posture with heightened accuracy but also provides immediate feedback to users through visual cues or notifications, fostering self-awareness and encouraging posture correction. The versatility and scalability of our solution make it applicable to diverse settings, including offices, healthcare, and education. This paper delves into the design, implementation, and challenges of our IoT-based system, emphasizing its potential to mitigate health risks linked to prolonged poor posture. By embracing advanced interdisciplinary approaches, we contribute to a more comprehensive understanding of posture-related complexities, paving the way for future advancements in public health through the promotion of better posture habits.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/37/e3sconf_icftest2024_01045.pdf |
| spellingShingle | Sreevani V. Sathwik Reddy B. Nithin K. Harsha Vardhan K. Mohammed Kahtan A. Reddy Uma Lakhanpal Sorabh Kalra Ravi Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT E3S Web of Conferences |
| title | Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT |
| title_full | Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT |
| title_fullStr | Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT |
| title_full_unstemmed | Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT |
| title_short | Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT |
| title_sort | advanced interdisciplinary approaches for bad posture detection using computer vision and iot |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/37/e3sconf_icftest2024_01045.pdf |
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