title: Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors.
abstract: Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user's sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest). Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples). Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.
authors.affiliation: Institute for Biomechanics, ETH Zurich, Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland.
authors.forename: Roland
authors.identifier: 0000-0003-4335-1217
authors.initials: R
authors.lastname: Zemp
authors.affiliation: Seminar for Statistics, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland.
authors.forename: Matteo
authors.initials: M
authors.lastname: Tanadini
authors.affiliation: Institute for Biomechanics, ETH Zurich, Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland.
authors.forename: Stefan
authors.initials: S
authors.lastname: Plüss
authors.affiliation: Institute for Biomechanics, ETH Zurich, Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland.
authors.forename: Karin
authors.initials: K
authors.lastname: Schnüriger
authors.affiliation: Institute for Biomechanics, ETH Zurich, Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland.
authors.forename: Navrag B
authors.initials: NB
authors.lastname: Singh
authors.affiliation: Institute for Biomechanics, ETH Zurich, Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland.
authors.forename: William R
authors.identifier: 0000-0003-4060-4098
authors.initials: WR
authors.lastname: Taylor
authors.affiliation: Institute for Biomechanics, ETH Zurich, Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland.
authors.forename: Silvio
authors.identifier: 0000-0002-8339-8960
authors.initials: S
authors.lastname: Lorenzetti
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods; D056044:Actigraphy / Q000295:instrumentation / Q000379:methods; D000328:Adult; D000465:Algorithms; D002081:Buttocks / Q000502:physiology; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D004856:Postural Balance / Q000502:physiology*; D011187:Posture / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014160:Transducers, Pressure; D055815:Young Adult