DocumentCode :
1156851
Title :
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
Author :
Ward, J.A. ; Lukowicz, P. ; Troster, G. ; Starner, T.E.
Author_Institution :
Inst. for Electron., Swiss Fed. Inst. of Technol., Zurich
Volume :
28
Issue :
10
fYear :
2006
Firstpage :
1553
Lastpage :
1567
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
Keywords :
accelerometers; assembling; hidden Markov models; maintenance engineering; microphones; production engineering computing; wearable computers; accelerometers; activity recognition; assembly tasks; body-worn microphones; hidden Markov models; linear discriminant analysis; maintenance work; wearable computer; Accelerometers; Assembly; Character recognition; Drilling; Hidden Markov models; Linear discriminant analysis; Microphones; Mobile computing; Sawing; Wearable computers; Pervasive computing; classifier evaluation; industry.; wearable computers and body area networks; Acceleration; Activities of Daily Living; Artificial Intelligence; Clothing; Human Engineering; Humans; Industry; Monitoring, Ambulatory; Motor Activity; Occupations; Pattern Recognition, Automated; Sound Spectrography; Task Performance and Analysis; Transducers; Workplace;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2006.197
Filename :
1677514
Link To Document :
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