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Behavior modeling in industrial assembly lines using a wrist-worn inertial measurement unit

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Abstract

In this study an approach to an imperceptible and reliable worker monitoring system for industrial assembly lines is presented. A single wrist-worn inertial measurement unit is attached to the active wrist of the worker and by using acceleration and angular speed information, the behavior of the worker is recognized. The recognition is done in two steps. First the data are divided into 2-s intervals in which the performed activity is recognized using a knn classifier so that the system is usable online. In the second step, a state machine is used to recognize the completed tasks by searching for continuous, unvarying activity chains. The approach was developed as user-independent, although it can be easily adapted to a user-dependent case. By using the approach, behavior was recognized correctly and, on average, the correct beginning and ending moments of the behavior were missed by only 1 s. Thus a reliable monitoring system can be developed for industrial assembly lines. This work was supported by the EU 6th Framework Program Project XPRESS.

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References

  • Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp 493–498

  • Ermes M, Pärkkä J, Mäntyjärvi J, Korhonen I (2008) Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans Inf Technol Biomed 12(1):20–26

    Article  Google Scholar 

  • Fix E, Hodges JL Jr (1951) Discriminatory analysis—nonparametric discrimination: consistency properties. Technical report 4, US Air Force, School of Aviation Medicine, Randolph Field, TX

  • Keogh E, Chu S, Hart D, Pazzani M (2001) An online algorithm for segmenting time series. In: Proceedings of IEEE international conference on data mining, pp 289–296

  • Koskimäki H, Huikari V, Siirtola P, Laurinen P, Röning J (2009) Activity recognition using a wrist-worn inertial measurement unit: a case study for industrial assembly lines. In: Proceedings of 17th IEEE Mediterranean conference on control and automation, pp 401–405

  • Mathie M, Coster A, Lovell N, Celler B (2003) Detection of daily physical activities using a triaxial accelerometer. Med Biol Eng Comput 41(3):296–301

    Article  Google Scholar 

  • Mitchell T (1997) Machine learning. The McGraw-Hill Companies, Inc.

  • Pirttikangas S, Fujinami K, Nakajima T (2006) Feature selection and activity recognition from wearable sensors. In: International symposium on ubiquitous computing systems (UCS2006), pp 516–527

  • SAMH Engineering Services (2009) Shake sk6 user manual

  • Siirtola P, Laurinen P, Haapalainen E, Röning J, Kinnunen H (2009) Clustering-based activity classification with a wrist-worn accelerometer using basic features. In: Proceedings of the 2009 IEEE symposium on computational intelligence in data mining, pp 95–100

  • Stiefmeier T, Roggen D, Tröster G, Ogris G, Lukowicz P (2008) Wearable activity tracking in car manufacturing. IEEE Pervasive Comput 7(2):42–50

    Article  Google Scholar 

  • Ward J, Lukowicz P, Tröster G (2005) Gesture spotting using wrist worn microphone and 3-axis accelerometer

  • Ward JA, Lukowicz P, Tröster G, Starner TE (2006) Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Trans Pattern Anal Mach Intell 28(10):1553–1567

    Article  Google Scholar 

  • Won SH, Golnaraghi F, Melek W (2009) A fastening tool tracking system using an imu and a position sensor with kalman filters and a fuzzy expert system. IEEE Trans Ind Electron 56(5):1782–1792. doi:10.1109/TIE.2008.2010166

    Google Scholar 

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Correspondence to Heli Koskimäki.

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Koskimäki, H., Huikari, V., Siirtola, P. et al. Behavior modeling in industrial assembly lines using a wrist-worn inertial measurement unit. J Ambient Intell Human Comput 4, 187–194 (2013). https://doi.org/10.1007/s12652-011-0061-3

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  • DOI: https://doi.org/10.1007/s12652-011-0061-3

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