A Hidden Semi-Markov Model for Predicting Production Cycle Time Using Bluetooth Low Energy Data


  • Karishma Agrawal Department of Electrical and Computer Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Thailand
  • Supachai Vorapojpisut Department of Electrical and Computer Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Thailand




Bluetooth low energy, received signal strength indicator, hidden semi-Markov model, learning problem


This study proposes a statistical model to characterize the temporal characteristics of an entire production process. The model utilizes received signal strength indicator (RSSI) data obtained from a Bluetooth low energy (BLE) network. A hidden semi-Markov model (HSMM) is formulated based on the characteristics of the production process, and the forward-backward algorithm is employed to re-estimate the probability distribution of state durations. The proposed method is validated through numerical, simulation, and real-world experiments, yielding promising results. The results show that the Kullback-Leibler divergence (KLD) score of 0.1843, while the simulation achieves an average vector distance score of 0.9740. The real-time experiment also shows a reasonable accuracy, with an average HSMM estimated throughput time of 30.48 epochs, compared to the average real throughput time of 33.99 epochs. Overall, the model serves as a valuable tool for predicting the cycle time and throughput time of a production line.


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How to Cite

Karishma Agrawal and Supachai Vorapojpisut, “A Hidden Semi-Markov Model for Predicting Production Cycle Time Using Bluetooth Low Energy Data”, Adv. technol. innov., vol. 8, no. 4, pp. 241–253, Sep. 2023.