NDLI: Activity recognition using a wrist-worn inertial measurement unit: A case study for industrial assembly lines

Content Provider	IEEE Xplore Digital Library
Author	Koskimaki, H. Huikari, V. Siirtola, P. Laurinen, P. Roning, J.
Copyright Year	2009
Description	Author affiliation: Intelligent Systems Group, University of Oulu, Po Box 4500, 90014, Finland (Koskimaki, H.; Huikari, V.; Siirtola, P.; Laurinen, P.; Roning, J.)
Abstract	As wearable sensors are becoming more common, their utilization in real-world applications is also becoming more attractive. In this study, a single wrist-worn inertial measurement unit was attached to the active wrist of a worker and acceleration and angular speed information was used to decide what activity the worker was performing at certain time intervals. This activity information can then be used for proactive instruction systems or to ensure that all the needed work phases are performed. In this study, the selected activities were basic tasks of hammering, screwing, spanner use and using a power drill for screwing. In addition, a null activity class consisting of other activities (moving around the post, staying still, changing tools) was defined. The performed activity could then be recognized online by using a sliding window method to divide the data into two-second intervals and overlapping two adjacent windows by 1.5 seconds. Thus, the activity was recognized every half second. The method used for the actual recognition was the k nearest neighbor method with a specific distance boundary for classifying completely new events as null data. In addition, the final class was decided by using a majority vote to classifications of three adjacent windows. The results showed that almost 90 percent accuracy can be achieved with this kind of setting; the activity-specific accuracies for hammering, screwing, spanner use, power drilling and null data were 96.4%, 89.7%, 89.5%, 77.6% and 89.0%, respectively. In addition, in a case with completely new null events, use of the specific distance measure improved accuracy from 68.6% to 82.3%.
Starting Page	401
Ending Page	405
File Size	238940
Page Count	5
File Format	PDF
ISBN	9781424446841
DOI	10.1109/MED.2009.5164574
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-24
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wrist Accelerometers Performance evaluation Drilling Measurement units Voting Acceleration Assembly Wearable sensors Nearest neighbor searches
Content Type	Text
Resource Type	Article

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