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Machine learning model for predicting shear forces at the body-seat interface in wheelchair users: A novel approach.

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Τίτλος: Machine learning model for predicting shear forces at the body-seat interface in wheelchair users: A novel approach.
Συγγραφείς: Clémence P; Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Centre Intégré universitaire de santéde services sociaux de la Capitale-Nationale, Québec, QC, Canada.; School of Rehabilitation Sciences, Faculty of Medicine, Université Laval, Québec, QC, Canada., Olivier C; Research and Development Department, Texisense, Torcy, France., Anthony G; Research and Development Department, Centre Mutualiste Neurologique Propara, Montpellier, France.; Research and Development Department, EPSYLON Laboratory, Montpellier, France., Ciancia S; Research and Development Department, Hopital Henry Gabriel, Hospice civil de Lyon, Lyon, France., Laura D; Research and Development Department, Université Lyon, Université Gustave Eiffel, Université Claude Bernard Lyon 1, Lyon, France., Sonia D; Research and Development Department, Université Lyon, Université Gustave Eiffel, Université Claude Bernard Lyon 1, Lyon, France.
Πηγή: Assistive technology : the official journal of RESNA [Assist Technol] 2026 Sep; Vol. 38 (5), pp. 475-483. Date of Electronic Publication: 2025 Dec 03.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Taylor & Francis Country of Publication: United States NLM ID: 8917250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1949-3614 (Electronic) Linking ISSN: 10400435 NLM ISO Abbreviation: Assist Technol Subsets: MEDLINE
Imprint Name(s): Publication: Philadelphia, PA : Taylor & Francis
Original Publication: [New York, NY] : Demos, [1989-
Ιατρικοί όροι (MeSH): Wheelchairs* , Machine Learning* , Sitting Position*, Pressure Ulcer/prevention & control ; Humans ; Predictive Learning Models ; Female ; Male ; Adult ; Biomechanical Phenomena ; Middle Aged ; Random Forest ; Pressure ; Equipment Design ; Prediction Algorithms
Περίληψη: Among the mechanical factors contributing to pressure injuries, shear forces at the body-seat interface play a critical role. This study introduces a novel machine learning approach to predict these forces, using data from pressure mapping systems and a multi-adjustable experimental seat. A supervised learning model was trained on measurements collected from individuals without disabilities and later evaluated on both this group and a cohort of wheelchair users. The selected model - a Random Forest Regression - relied on six input features: a calculated variable, backrest force, feet normal force, seat pan force, backrest area, and the location of the backrest center of pressure. It demonstrated promising accuracy, with an average error below 20% for individuals without disabilities and for wheelchair users whose shear forces were within a similar range. However, performance declined for wheelchair users exhibiting significantly lower shear forces. To improve generalizability, future work will expand the dataset to include participants with more diverse anthropometric characteristics and a broader range of seated postures.
Contributed Indexing: Keywords: Machine learning; prediction; pressure injury; shear force; wheelchair user
Entry Date(s): Date Created: 20251203 Date Completed: 20260827 Latest Revision: 20260905
Update Code: 20260905
DOI: 10.1080/10400435.2025.2587794
PMID: 41335345
Βάση Δεδομένων: MEDLINE
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  Data: Machine learning model for predicting shear forces at the body-seat interface in wheelchair users: A novel approach.
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  Data: <searchLink fieldCode="AU" term="%22Clémence+P%22">Clémence P</searchLink>; Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Centre Intégré universitaire de santéde services sociaux de la Capitale-Nationale, Québec, QC, Canada.; School of Rehabilitation Sciences, Faculty of Medicine, Université Laval, Québec, QC, Canada.<br /><searchLink fieldCode="AU" term="%22Olivier+C%22">Olivier C</searchLink>; Research and Development Department, Texisense, Torcy, France.<br /><searchLink fieldCode="AU" term="%22Anthony+G%22">Anthony G</searchLink>; Research and Development Department, Centre Mutualiste Neurologique Propara, Montpellier, France.; Research and Development Department, EPSYLON Laboratory, Montpellier, France.<br /><searchLink fieldCode="AU" term="%22Ciancia+S%22">Ciancia S</searchLink>; Research and Development Department, Hopital Henry Gabriel, Hospice civil de Lyon, Lyon, France.<br /><searchLink fieldCode="AU" term="%22Laura+D%22">Laura D</searchLink>; Research and Development Department, Université Lyon, Université Gustave Eiffel, Université Claude Bernard Lyon 1, Lyon, France.<br /><searchLink fieldCode="AU" term="%22Sonia+D%22">Sonia D</searchLink>; Research and Development Department, Université Lyon, Université Gustave Eiffel, Université Claude Bernard Lyon 1, Lyon, France.
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  Data: <searchLink fieldCode="JN" term="%228917250%22">Assistive technology : the official journal of RESNA</searchLink> [Assist Technol] 2026 Sep; Vol. 38 (5), pp. 475-483. <i>Date of Electronic Publication: </i>2025 Dec 03.
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  Data: <searchLink fieldCode="MM" term="%22Wheelchairs%22">Wheelchairs*</searchLink> <br /><searchLink fieldCode="MM" term="%22Machine+Learning%22">Machine Learning*</searchLink> <br /><searchLink fieldCode="MM" term="%22Sitting+Position%22">Sitting Position*</searchLink><br /><searchLink fieldCode="MH" term="%22Pressure+Ulcer%22">Pressure Ulcer</searchLink>/<searchLink fieldCode="MH" term="%22Pressure+Ulcer+prevention+%26+control%22">prevention & control</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Predictive+Learning+Models%22">Predictive Learning Models</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Adult%22">Adult</searchLink> ; <searchLink fieldCode="MH" term="%22Biomechanical+Phenomena%22">Biomechanical Phenomena</searchLink> ; <searchLink fieldCode="MH" term="%22Middle+Aged%22">Middle Aged</searchLink> ; <searchLink fieldCode="MH" term="%22Random+Forest%22">Random Forest</searchLink> ; <searchLink fieldCode="MH" term="%22Pressure%22">Pressure</searchLink> ; <searchLink fieldCode="MH" term="%22Equipment+Design%22">Equipment Design</searchLink> ; <searchLink fieldCode="MH" term="%22Prediction+Algorithms%22">Prediction Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Among the mechanical factors contributing to pressure injuries, shear forces at the body-seat interface play a critical role. This study introduces a novel machine learning approach to predict these forces, using data from pressure mapping systems and a multi-adjustable experimental seat. A supervised learning model was trained on measurements collected from individuals without disabilities and later evaluated on both this group and a cohort of wheelchair users. The selected model - a Random Forest Regression - relied on six input features: a calculated variable, backrest force, feet normal force, seat pan force, backrest area, and the location of the backrest center of pressure. It demonstrated promising accuracy, with an average error below 20% for individuals without disabilities and for wheelchair users whose shear forces were within a similar range. However, performance declined for wheelchair users exhibiting significantly lower shear forces. To improve generalizability, future work will expand the dataset to include participants with more diverse anthropometric characteristics and a broader range of seated postures.
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  Data: <i>Keywords: </i>Machine learning; prediction; pressure injury; shear force; wheelchair user
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  Data: <i>Date Created: </i>20251203 <i>Date Completed: </i>20260827 <i>Latest Revision: </i>20260905
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        Value: 10.1080/10400435.2025.2587794
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      – Code: eng
        Text: English
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        StartPage: 475
    Subjects:
      – SubjectFull: Pressure Ulcer prevention & control
        Type: general
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Predictive Learning Models
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      – SubjectFull: Middle Aged
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      – SubjectFull: Pressure
        Type: general
      – SubjectFull: Equipment Design
        Type: general
      – SubjectFull: Prediction Algorithms
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      – SubjectFull: Wheelchairs
        Type: general
      – SubjectFull: Machine Learning
        Type: general
      – SubjectFull: Sitting Position
        Type: general
    Titles:
      – TitleFull: Machine learning model for predicting shear forces at the body-seat interface in wheelchair users: A novel approach.
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              M: 09
              Text: 2026 Sep
              Type: published
              Y: 2026
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