Anthropometry measurements of farm workers using computer vision-based multiview stereo-image sensing.
Background: Precise anthropometric data are vital for ergonomic assessment and farm machinery design. Manual methods, although dependable, are labor-intensive and susceptible to error. Objective: This study aimed to develop and validate a computer vision (CV) based non-contact system for anthropomet...
| Publicado en: | Work Vol. 84; no. 3; pp. 802 - 815 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194971345&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194971345 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: Jul2026 vid: 84 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194971345 191692129 194971345 194971345 10.1177/10519815261421913 194971345 ppf: 802 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Anthropometry measurements of farm workers using computer vision-based multiview stereo-image sensing. aug: au: Lohan, Shiv Kumar K, Kashish Lohan, Navjeet Singh, Harmandeep affil: Department of Farm Machinery & Power Engineering, Punjab Agricultural University, Ludhiana, India sug: subj: Anthropometry Farmworkers Imaging, Three-Dimensional Utilization Body Height Evaluation Ergonomics Hip Joint Analysis Thorax Analysis Body Weights and Measures Program Development Human Sensitivity and Specificity Occupational Health Male Female Algorithms Power Analysis Descriptive Statistics Data Analysis Software Intraclass Correlation Coefficient Funding Source Videorecording Image Processing, Computer Assisted Equipment Design Agriculture Equipment and Supplies Male Female ab: Background: Precise anthropometric data are vital for ergonomic assessment and farm machinery design. Manual methods, although dependable, are labor-intensive and susceptible to error. Objective: This study aimed to develop and validate a computer vision (CV) based non-contact system for anthropometric measurements, focusing on stature, vertical reach, trochanteric height, and chest circumference. Methods: An Intel RealSense D435i stereo camera with OpenCV, mediapipe captured images from three angles (front, diagonal, side) at 2.5–3.5 m. Thirty-two participants (16 male, 16 female) were measured, with manual anthropometry as reference. Accuracy was assessed using mean absolute difference (MAD) and mean absolute percentage error (MAPE), while reliability was examined via intraclass correlation coefficient (ICC, p < 0.05). Results: The 3.0 m front-facing view yielded the most accurate measurements. CV-based anthropometry slightly underestimated stature for males (1596 vs. 1646 mm) and females (1456 vs. 1521 mm; MAD 53–65 mm; MAPE 3–4%), with excellent reliability (ICC > 0.90, α > 0.85). Vertical reach showed the largest bias (83–90 mm; MAPE 4–5%), yet reliability remained high (ICC 0.88–0.91). Trochanteric height had minimal discrepancies (29–36 mm; MAPE ≤ 4%) with good consistency (ICC 0.85–0.90). Chest circumference showed small bias (±10 mm; MAPE 3–4%) but lower reliability (ICC 0.75–0.80), likely due to respiration. Overall, CV measurements were reliable, non-invasive, and scalable. Conclusions: The CV-based system offers a precise, scalable, and non-contact alternative to manual anthropometry, enabling reliable data collection for ergonomic evaluation and improved man–machine compatibility in agriculture. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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