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Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers

Ward, Jamie A and Lukowicz, Paul and Troster, Gerhard and Starner, Thad E. (2006) Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (10). pp. 1553-1567. ISSN 0162-8828

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    Abstract

    In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user's specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock "wood workshop” assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, hammering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and three-axis accelerometers mounted at two positions on the user's arms. Potentially "interesting” activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov models (HMMs) on the acceleration data. Four different methods at classifier fusion are compared for improving these classifications. Using user-dependent training, we obtain continuous average recall and precision rates (for positive activities) of 78 percent and 74 percent, respectively. Using user-independent training (leave-one-out across five users), we obtain recall rates of 66 percent and precision rates of 63 percent. In isolation, these activities were recognized with accuracies of 98 percent, 87 percent, and 95 percent for the user-dependent, user-independent, and user-adapted cases, respectively.

    Item Type: Article
    Journal or Publication Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
    Uncontrolled Keywords: cs_eprint_id ; 1624 cs_uid ; 382
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 13082
    Deposited By: ep_importer_comp
    Deposited On: 18 Jul 2008 10:55
    Refereed?: Yes
    Published?: Published
    Last Modified: 29 Oct 2012 16:37
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/13082

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