4th International ICST Conference on Pervasive Computing Technologies for Healthcare

Research Article

AID-ME: Automatic identification of dressing failures through monitoring of patients and activity Evaluation

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  • @INPROCEEDINGS{10.4108/ICST.PERVASIVEHEALTH2010.8895,
        author={Aleksandar Matic and Priyal Mehta and James M. Rehg and Venet Osmani and Oscar Mayora},
        title={AID-ME: Automatic identification of dressing failures through monitoring of patients and activity Evaluation},
        proceedings={4th International ICST Conference on Pervasive Computing Technologies for Healthcare},
        proceedings_a={PERVASIVEHEALTH},
        year={2010},
        month={6},
        keywords={Aging Computerized monitoring Condition monitoring Dementia Diseases Medical services Medical treatment Patient monitoring Radiofrequency identification Senior citizens},
        doi={10.4108/ICST.PERVASIVEHEALTH2010.8895}
    }
    
  • Aleksandar Matic
    Priyal Mehta
    James M. Rehg
    Venet Osmani
    Oscar Mayora
    Year: 2010
    AID-ME: Automatic identification of dressing failures through monitoring of patients and activity Evaluation
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/ICST.PERVASIVEHEALTH2010.8895
Aleksandar Matic1,*, Priyal Mehta2,*, James M. Rehg2,*, Venet Osmani1,*, Oscar Mayora1,*
  • 1: Ubiquitous Interaction Group, CREATE-NET, via alla Cascata 56/D, Povo, Trento, Italy
  • 2: Health Systems Institute and GVU Center, School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA
*Contact email: aleksandar.matic@create-net.org, priyal@cc.gatech.edu, rehg@cc.gatech.edu, venet.osmani@create-net.org, oscar.mayora@create-net.org

Abstract

Monitoring and evaluation of Activities of Daily Living in general, and dressing activity in particular, is an important indicator in the evaluation of the overall cognitive state of patients. In addition, the effectiveness of therapy in patients with motor impairments caused by a stroke, for example, can be measured through long-term monitoring of dressing activity. However, monitoring of dressing activity has not received significant attention. In this paper, we describe a system that can automatically monitor dressing activity and identify dressing failures exhibited by patients. The system uses a synergistic combination of RFID and computer vision in order to identify a number of common dressing failures exhibited by the patients.