6th International ICST Conference on Body Area Networks

Research Article

Real-Time, Model Based Algorithm Implementation for Human Posture Classification

Download573 downloads
  • @INPROCEEDINGS{10.4108/icst.bodynets.2011.247123,
        author={Mohammed Aloqlah and Rosa Lahiji and Mehran Mehregany},
        title={Real-Time, Model Based Algorithm Implementation for Human Posture Classification},
        proceedings={6th International ICST Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2012},
        month={6},
        keywords={real-time classification kinematic model discrete wavelet transform fuzzy logic inference system},
        doi={10.4108/icst.bodynets.2011.247123}
    }
    
  • Mohammed Aloqlah
    Rosa Lahiji
    Mehran Mehregany
    Year: 2012
    Real-Time, Model Based Algorithm Implementation for Human Posture Classification
    BODYNETS
    ICST
    DOI: 10.4108/icst.bodynets.2011.247123
Mohammed Aloqlah1,*, Rosa Lahiji2, Mehran Mehregany2
  • 1: Yarmouk University
  • 2: Case Western Reserve University
*Contact email: mohamads@yu.edu.jo

Abstract

A generic platform for continuously and unobtrusively monitoring human motion activity is deployed. Wirelessly transmitted data from a single three-axis accelerometer integrated into the headband is collected in real time on a laptop, and then analyzed to extract two sets of features necessary for posture/movement classification. The received acceleration signals is decomposed with discrete wavelet transform (DWT) to extract the first set of features; any change of the smoothness of the signal that reflects a transition between postures is detected at the finer DWT resolution levels. Fuzzy logic inference system (FIS) then uses the previous posture transition and the second set of features to choose one of eight different posture categories, namely sit, stand, lie on back, lie on left, lie on right, bend, walk, and run. Using the classifier in typical everyday activity among multiple users indicated more than 96.9%, 94.2%, 97.5% accuracy in detecting the static postures, walking, and running, respectively. Identifying the dynamic transitions among these steady postures achieved 92.6% accuracy.