UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Activity recognition of assembly tasks using body-worn microphones and accelerometers

Ward, JA; Lukowicz, P; Troester, G; Starner, TE; (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. 10.1109/TPAMI.2006.197. Green open access

[img]
Preview
Text
Ward_activity-ward-pami-2006.pdf

Download (702kB) | Preview

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 3-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% and 74%, respectively. Using user-independent training (leave-one-out across five users), we obtain recall rates of 66% and precision rates of 63%. In isolation, these activities were recognized with accuracies of 98%, 87%, and 95% for the user-dependent, userindependent, and user-adapted cases, respectively.

Type: Article
Title: Activity recognition of assembly tasks using body-worn microphones and accelerometers
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TPAMI.2006.197
Publisher version: https://doi.org/10.1109/TPAMI.2006.197
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, pervasive computing, wearable computers and body area networks, classifier evaluation, industry, PHYSICAL-ACTIVITY, CLASSIFIERS, VALIDATION, SYSTEMS
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/1535730
Downloads since deposit
142Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item