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Automated worker skill evaluation for improving productivity based on labeled LDA

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Abstract

This paper proposed automated systems for analyzing elemental processes and for evaluating work skills. The systems use labeled latent Dirichlet allocation (L-LDA) to classify worker motions obtained from sensors into four elemental processes. L-LDA automatically learns characteristic motions, so there is no need to define and identify motion features. The proposed system predicts elemental processes with over 86.9% recall in experiments using the assembly process data. Analyst burden is greatly reduced as compared to systems requiring manual analysis of elemental processes from recorded task data. The system evaluates worker skills based on analyzed time series data for elemental processes in four categories, namely, correctness, stability, speed, and rhythm. As a result, the evaluation system clarifies workers’ strong and weak points in tasks performed in experiments, providing new knowledge that would be unobtainable under conventional evaluation methods. Manufacturing efficiency can be improved by allocating workers based on their strengths, and training efficiency will be improved when workers’ weak areas are revealed.

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Acknowledgements

The authors thank Y. Kotake and D. Wang from Omron Corporation for their helpful discussions and for sharing their dataset with us. This work was supported in part by the Tateisi Science and Technology Foundation (Grant no. 2197012).

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Authors and Affiliations

  1. University of Hyogo, Kobe, Japan

    Kentaro Mori & Yutaka Hata

  2. Omron Corporation, Kizugawa, Japan

    Hiroshi Nakajima

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  1. Kentaro Mori
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  2. Hiroshi Nakajima
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  3. Yutaka Hata
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Correspondence to Kentaro Mori.

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Appendix

Appendix

Figures 34, 35, 36, 37, 38, 39 show proposed algorithm as an appendix. These figures are shown based on Python code. Figure 34 shows the algorithm of converting data to text. Figure 35 shows the algorithm of predicting the elemental processes by L-LDA. Figure 36 shows the algorithm of categorizing the processes for each part. Figure 37 shows the algorithm of function “Part0”. Figure 38 shows the algorithm of function “Part1”. Figure 39 shows the algorithm of evaluating the worker skills.

Fig. 34
figure 34

Algorithm of converting data to text

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Fig. 35
figure 35

Algorithm of predicting the elemental processes by L-LDA

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Fig. 36
figure 36

Algorithm of categorizing the processes for each part

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Fig. 37
figure 37

Algorithm of function “Part0”

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Fig. 38
figure 38

Algorithm of function “Part1”

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Fig. 39
figure 39

Algorithm of evaluating the worker skills

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Mori, K., Nakajima, H. & Hata, Y. Automated worker skill evaluation for improving productivity based on labeled LDA. Int. J. Mach. Learn. & Cyber. 12, 1151–1171 (2021). https://doi.org/10.1007/s13042-020-01226-z

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