Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model
Posture-related musculoskeletal issues among office workers are a significant health concern, mainly due to long periods spent in static positions. This research presents a Posture Lab which is a workplace-based solution through an easy-to-use posture monitoring system, allowing employees to assess...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Wearable Technologies |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2631717625100054/type/journal_article |
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| author | Supachai Vorapojpisut Suphawit Sansuk Phoomtai Yindee Darawadee Panich Vinitha Puengtanom Sairag Saadprai |
| author_facet | Supachai Vorapojpisut Suphawit Sansuk Phoomtai Yindee Darawadee Panich Vinitha Puengtanom Sairag Saadprai |
| author_sort | Supachai Vorapojpisut |
| collection | DOAJ |
| description | Posture-related musculoskeletal issues among office workers are a significant health concern, mainly due to long periods spent in static positions. This research presents a Posture Lab which is a workplace-based solution through an easy-to-use posture monitoring system, allowing employees to assess their posture. The Posture Lab focuses on two key aspects: Normal Head Posture (NHP) versus Forward Head Posture (FHP) measurement and thoracic spine kyphosis. Craniovertebral (CA) and Shoulder Angles (SA) quantify NHP and FHP. The Kyphosis Angle (KA) is for measuring normal thoracic spine and kyphosis. To measure these angles, the system uses computer vision technology with ArUco markers detection via a webcam to analyze head positions. Additionally, wearable accelerometer sensors measure kyphosis by checking the angles of inclination. The framework includes a web-based user interface for registration and specialized desktop applications for different measurement protocols. A RESTful API enables system communication and centralized data storage for reporting. The Posture Lab serves as an effective tool for organizations to evaluate employee postures and supports early intervention strategies, allowing timely referrals to healthcare providers if any potential musculoskeletal issues are identified. The Posture Lab has also shown medium to very high correlations with standard 2D motion analysis methods – Kinovea – for CA, SA, and KA in FHP with kyphosis measurements (r = 0.607, 0.704, and 0.992) and shown high to very high correlations in NHP with normal thoracic spine measurements (r = 0.809, 0.748, and 0.778), with significance at p < .01, utilizing the Pearson correlation coefficient. |
| format | Article |
| id | doaj-art-848303700f8e454f99fbd14bf09bc89b |
| institution | OA Journals |
| issn | 2631-7176 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Wearable Technologies |
| spelling | doaj-art-848303700f8e454f99fbd14bf09bc89b2025-08-20T02:06:40ZengCambridge University PressWearable Technologies2631-71762025-01-01610.1017/wtc.2025.10005Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin modelSupachai Vorapojpisut0https://orcid.org/0000-0002-6320-9130Suphawit Sansuk1Phoomtai Yindee2Darawadee Panich3Vinitha Puengtanom4Sairag Saadprai5Faculty of Engineering, Thammasat School of Engineering, https://ror.org/002yp7f20 Thammasat University , Pathumthani, ThailandFaculty of Engineering, Thammasat School of Engineering, https://ror.org/002yp7f20 Thammasat University , Pathumthani, ThailandFaculty of Engineering, Thammasat School of Engineering, https://ror.org/002yp7f20 Thammasat University , Pathumthani, ThailandFaculty of Engineering, Thammasat School of Engineering, https://ror.org/002yp7f20 Thammasat University , Pathumthani, ThailandFaculty of Allied Health Sciences, Thammasat University, Pathumthani, ThailandFaculty of Allied Health Sciences, Thammasat University, Pathumthani, ThailandPosture-related musculoskeletal issues among office workers are a significant health concern, mainly due to long periods spent in static positions. This research presents a Posture Lab which is a workplace-based solution through an easy-to-use posture monitoring system, allowing employees to assess their posture. The Posture Lab focuses on two key aspects: Normal Head Posture (NHP) versus Forward Head Posture (FHP) measurement and thoracic spine kyphosis. Craniovertebral (CA) and Shoulder Angles (SA) quantify NHP and FHP. The Kyphosis Angle (KA) is for measuring normal thoracic spine and kyphosis. To measure these angles, the system uses computer vision technology with ArUco markers detection via a webcam to analyze head positions. Additionally, wearable accelerometer sensors measure kyphosis by checking the angles of inclination. The framework includes a web-based user interface for registration and specialized desktop applications for different measurement protocols. A RESTful API enables system communication and centralized data storage for reporting. The Posture Lab serves as an effective tool for organizations to evaluate employee postures and supports early intervention strategies, allowing timely referrals to healthcare providers if any potential musculoskeletal issues are identified. The Posture Lab has also shown medium to very high correlations with standard 2D motion analysis methods – Kinovea – for CA, SA, and KA in FHP with kyphosis measurements (r = 0.607, 0.704, and 0.992) and shown high to very high correlations in NHP with normal thoracic spine measurements (r = 0.809, 0.748, and 0.778), with significance at p < .01, utilizing the Pearson correlation coefficient.https://www.cambridge.org/core/product/identifier/S2631717625100054/type/journal_articlemusculoskeletal disordersprimary health carecomputer visionArUco markerswearable sensors |
| spellingShingle | Supachai Vorapojpisut Suphawit Sansuk Phoomtai Yindee Darawadee Panich Vinitha Puengtanom Sairag Saadprai Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model Wearable Technologies musculoskeletal disorders primary health care computer vision ArUco markers wearable sensors |
| title | Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model |
| title_full | Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model |
| title_fullStr | Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model |
| title_full_unstemmed | Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model |
| title_short | Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model |
| title_sort | quantifying sitting posture a pilot feasibility study of computer vision and wearable sensors posture lab using a manikin model |
| topic | musculoskeletal disorders primary health care computer vision ArUco markers wearable sensors |
| url | https://www.cambridge.org/core/product/identifier/S2631717625100054/type/journal_article |
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