title: Physical activity classification utilizing SenseWear activity monitor in manual wheelchair users with spinal cord injury.
abstract: METHODS
  Validation.
  
  
  OBJECTIVE
  The primary aim of this study was to develop and evaluate activity classification algorithms for a multisensor-based SenseWear (SW) activity monitor that can recognize wheelchair-related activities performed by manual wheelchair users (MWUs) with spinal cord injury (SCI). The secondary aim was to evaluate how the accuracy in activity classification affects the estimation of energy expenditure (EE) in MWUs with SCI.
  
  
  METHODS
  University-based laboratory.
  
  
  METHODS
  Forty-five MWUs with SCI wore a SW on their upper arm and participated in resting, wheelchair propulsion, arm-ergometery and deskwork activities. The investigators annotated the start and end of each activity trial while the SW collected multisensor data and a portable metabolic cart collected criterion EE. Three methods including linear discriminant analysis, quadratic discriminant analysis (QDA), and Naïve Bayes (NB) were used to develop classification algorithms for four activities based on the training data set from 36 subjects.
  
  
  RESULTS
  The classification accuracy was 96.3% for QDA and 94.8% for NB when the classification algorithms were tested on the validation data set from nine subjects. The average EE estimation errors using the activity-specific EE prediction model were 5.3±21.5% and 4.6±22.8% when the QDA and NB classification algorithms were applied, respectively, as opposed to 4.9±20.7% when 100% classification accuracy was assumed.
  
  
  CONCLUSIONS
  The high classification accuracy and low EE estimation errors suggest that the SW can be used by researchers and clinicians to classify and estimate the EE for the four activities tested in this study among MWUs with SCI.
authors.affiliation: Department of Veterans Affairs, Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA.
authors.forename: S V
authors.initials: SV
authors.lastname: Hiremath
authors.forename: D
authors.initials: D
authors.lastname: Ding
authors.forename: J
authors.initials: J
authors.lastname: Farringdon
authors.forename: N
authors.initials: N
authors.lastname: Vyas
authors.forename: R A
authors.initials: RA
authors.lastname: Cooper
mesh_terms: D000328:Adult; D000465:Algorithms; D001132:Arm / Q000502:physiology; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004734:Energy Metabolism; D016552:Ergometry; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009043:Motor Activity / Q000502:physiology*; D010264:Paraplegia / Q000503:physiopathology; D011237:Predictive Value of Tests; D011782:Quadriplegia / Q000503:physiopathology; D015203:Reproducibility of Results; D013119:Spinal Cord Injuries; D014910:Wheelchairs