Multi-label classification for the analysis of human motion quality.
Knowing how well an activity is performed is important for home rehabilitation. We would like to not only know if a motion is being performed correctly, but also in what way the motion is incorrect so that we may provide feedback to the user. This paper describes methods for assessing human motion quality using body-worn tri-axial accelerometers and gyroscopes. We use multi-label classifiers to detect subtle errors in exercise performances of eight individuals with knee osteoarthritis, a degenerative disease of the cartilage. We present results obtained using various machine learning methods with decision tree base classifiers. The classifier can detect classes in multi-label data with 75% sensitivity, 90% specificity and 80% accuracy. The methods presented here form the basis for an at-home rehabilitation device that will recognize errors in patient exercise performance, provide appropriate feedback on the performance, and motivate the patient to continue the prescribed regimen.
Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. pet@cs.cmu.edu
Portia E
PE
Taylor
Gustavo J M
GJ
Almeida
Jessica K
JK
Hodgins
Takeo
T
Kanade
D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D009068:Movement; D020370:Osteoarthritis, Knee / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis