1. Biomechanical monitoring and machine learning for the detection of lying postures
- Author
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Peter Worsley, Silvia Caggiari, Marek Bucki, Yohan Payan, Dan L. Bader, University of Southampton, Ingénierie Biomédicale et Mécanique des Matériaux (TIMC-IMAG-BioMMat), Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications Grenoble - UMR 5525 (TIMC-IMAG), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Gestes Médico-chirurgicaux Assistés par Ordinateur (TIMC-IMAG-GMCAO), S.A.S. Texisense, entreprise, and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
- Subjects
Male ,Computer science ,Movement ,Posture ,Biophysics ,FOS: Physical sciences ,Machine learning ,computer.software_genre ,Signal ,Continuous pressure monitoring ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Range (statistics) ,Humans ,Orthopedics and Sports Medicine ,[PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph] ,Mechanical Phenomena ,Postures detection 31 32 ,business.industry ,Actimetry systems ,[SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph] ,Monitoring system ,Bayes Theorem ,030229 sport sciences ,Physics - Medical Physics ,Sagittal plane ,Biomechanical Phenomena ,Support vector machine ,medicine.anatomical_structure ,Postures detection ,[SDV.IB]Life Sciences [q-bio]/Bioengineering ,Medical Physics (physics.med-ph) ,Artificial intelligence ,Support surface ,business ,Lying ,computer ,030217 neurology & neurosurgery ,Test data - Abstract
Background: pressure mapping technology has been adapted to monitor over prolonged periods to evaluate pressure ulcer risk in individuals during extended lying postures. However, temporal pressure distribution signals are not currently used to identify posture or mobility. The present study was designed to examine the potential of an automated approach for the detection of a range of staticlying postures and corresponding transitions between postures.Methods: healthy subjects (n=19) adopted a range of sagittal and lateral lying postures. Parameters reflecting both the interactions at the support surface and body movements were continuously monitored. Subsequently, the derivative of each signal was examined to identify transitions between postures. Three machine learning algorithms, namely Naïve-Bayes, k-Nearest Neighbors and Support Vector Machine classifiers, were assessed to predict a range of static postures, established with a training model (n=9) and validated with new input from test data (n=10). Findings: results showed that the derivative signals provided a means to detect transitions between postures, with actimetry providing the most distinct signal perturbations. The accuracy in predicting the range of postures from new test data ranged between 82%-100%, 70%-98% and 69%-100% for Naïve-Bayes, k-Nearest Neighbors and Support Vector Machine classifiers, respectively.Interpretation: the present study demonstrated that detection of both static postures and their corresponding transitions was achieved by combining machine learning algorithms with robust parameters from two monitoring systems. This approach has the potential to provide reliable indicators of posture and mobility, to support personalized pressure ulcer prevention strategies.
- Published
- 2020