title: Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers.
abstract: METHODS
  This was a cross-sectional study.
  
  
  OBJECTIVE
  The main objective of this study was to develop and test classification algorithms based on machine learning using accelerometers to identify the activity type performed by manual wheelchair users with spinal cord injury (SCI).
  
  
  METHODS
  The study was conducted in the Physical Therapy department and the Physical Education and Sports department of the University of Valencia.
  
  
  METHODS
  A total of 20 volunteers were asked to perform 10 physical activities, lying down, body transfers, moving items, mopping, working on a computer, watching TV, arm-ergometer exercises, passive propulsion, slow propulsion and fast propulsion, while fitted with four accelerometers placed on both wrists, chest and waist. The activities were grouped into five categories: sedentary, locomotion, housework, body transfers and moderate physical activity. Different machine learning algorithms were used to develop individual and group activity classifiers from the acceleration data for different combinations of number and position of the accelerometers.
  
  
  RESULTS
  We found that although the accuracy of the classifiers for individual activities was moderate (55-72%), with higher values for a greater number of accelerometers, grouped activities were correctly classified in a high percentage of cases (83.2-93.6%).
  
  
  CONCLUSIONS
  With only two accelerometers and the quadratic discriminant analysis algorithm we achieved a reasonably accurate group activity recognition system (>90%). Such a system with the minimum of intervention would be a valuable tool for studying physical activity in individuals with SCI.
authors.affiliation: Departamento de Didáctica de la Expresión Musical, Plástica y Corporal, Universidad de Valencia, Valencia, Spain.
authors.forename: X
authors.initials: X
authors.lastname: García-Massó
authors.affiliation: Departamento de Fisioterapia, Universidad de Valencia, Valencia, Spain.
authors.forename: P
authors.initials: P
authors.lastname: Serra-Añó
authors.affiliation: Departamento de Educación Física y Deportiva, Universidad de Valencia, Valencia, Spain.
authors.forename: L M
authors.initials: LM
authors.lastname: Gonzalez
authors.affiliation: Grupo de Bioelectrónica (I3BH), Universitat Politècnica de València, Camino de Vera s/n Ed.8B, Valencia, Spain.
authors.forename: Y
authors.initials: Y
authors.lastname: Ye-Lin
authors.affiliation: Grupo de Bioelectrónica (I3BH), Universitat Politècnica de València, Camino de Vera s/n Ed.8B, Valencia, Spain.
authors.forename: G
authors.initials: G
authors.lastname: Prats-Boluda
authors.affiliation: Grupo de Bioelectrónica (I3BH), Universitat Politècnica de València, Camino de Vera s/n Ed.8B, Valencia, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Garcia-Casado
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods*; D000368:Aged; D003430:Cross-Sectional Studies; D016002:Discriminant Analysis; D007839:Functional Laterality; D006801:Humans; D009043:Motor Activity / Q000502:physiology; D015203:Reproducibility of Results; D013119:Spinal Cord Injuries / Q000145:classification* / Q000503:physiopathology / Q000534:rehabilitation*; D060388:Support Vector Machine; D014910:Wheelchairs; D014953:Wrist / Q000503:physiopathology