4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"

Research Article

Automated Diagnosis of Knee Pathology Using Sensory Data

Download713 downloads
  • @INPROCEEDINGS{10.4108/icst.mobihealth.2014.257526,
        author={Majid Janidarmian and Katarzyna Radecka and Zeljko Zilic},
        title={Automated Diagnosis of Knee Pathology Using Sensory Data},
        proceedings={4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"},
        publisher={IEEE},
        proceedings_a={MOBIHEALTH},
        year={2014},
        month={12},
        keywords={knee pathology classification feature extraction surface emg goniometer},
        doi={10.4108/icst.mobihealth.2014.257526}
    }
    
  • Majid Janidarmian
    Katarzyna Radecka
    Zeljko Zilic
    Year: 2014
    Automated Diagnosis of Knee Pathology Using Sensory Data
    MOBIHEALTH
    IEEE
    DOI: 10.4108/icst.mobihealth.2014.257526
Majid Janidarmian1,*, Katarzyna Radecka1, Zeljko Zilic1
  • 1: McGill University
*Contact email: majid.janidarmian@gmail.com

Abstract

In order to early diagnosis and treatment of knee abnormalities, in this study an automated diagnosis system using wearable EMG and goniometer sensors is proposed. Eight different classification techniques are investigated with a set of time-domain features. The experiments are conducted with 22 subjects’ data and the best accuracy of 97.17% is achieved based on the Bagged Decision Trees classifier. We have also evaluated the classifications quality with Fixed-size Overlapping Sliding Window (FOSW) segmentation technique where SVM and Bagged Decision Trees classifiers could obtain the accuracy of 100% in distinguishing healthy subjects from people with knee abnormality.