PervaSense WORKSHOP (PervasiveHealth) 2010

Research Article

A long-term sensory logging device for subject monitoring

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  • @INPROCEEDINGS{10.4108/ICST.PERVASIVEHEALTH2010.8826,
        author={Dawud Gordon and Florian Witt and Hedda Schmidtke and Michael Beigl},
        title={A long-term sensory logging device for subject monitoring},
        proceedings={PervaSense WORKSHOP (PervasiveHealth) 2010},
        proceedings_a={PERVASENSE},
        year={2010},
        month={6},
        keywords={Accelerometers Application software Biomedical monitoring Data preprocessing Level control Measurement units Medical services Medical tests Patient monitoring Temperature sensors},
        doi={10.4108/ICST.PERVASIVEHEALTH2010.8826}
    }
    
  • Dawud Gordon
    Florian Witt
    Hedda Schmidtke
    Michael Beigl
    Year: 2010
    A long-term sensory logging device for subject monitoring
    PERVASENSE
    IEEE
    DOI: 10.4108/ICST.PERVASIVEHEALTH2010.8826
Dawud Gordon1,*, Florian Witt1,*, Hedda Schmidtke1,*, Michael Beigl1,*
  • 1: Distributed and Ubiquitous Systems, Technische Universitat Braunschweig, Braunschweig, Germany
*Contact email: gordon@ibr.cs.tu-bs.de, fwitt@ibr.cs.tu-bs.de, schmidtke@ibr.cs.tu-bs.de, beigl@ibr.cs.tu-bs.de

Abstract

In this paper we introduce a device for monitoring subjects by logging sensory data over long periods of time. The system consists of a sensory measurement unit, a memory unit and an application for data preprocessing tasks, such as converting sensory measurements to desired units or calculating averages. In order to demonstrate usage, an activity level monitoring system inspired by medical applications is implemented using the device. A rudimentary threshold-based embedded classifier is trained using several different activities, yielding an activity level indicator. 2 subjects are monitored to train the classifier, and the system is then evaluated on new data using those two subjects plus a third not involved in the training process. The results indicate an activity classification of 74% into three levels using 2 simple data thresholds, with a system lifetime of 26 days on 2 AAA batteries.