_Applied_  
_sciences_  
_An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems_  
**[PERSON] \({}^{1,*}\)\({}^{\bigcirc}\), [PERSON] \({}^{1}\)\({}^{\bigcirc}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\)\({}^{\bigcirc}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{4}\)**  
\({}^{1}\) Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania; [EMAIL_ADDRESS] (S.-A.P.); [EMAIL_ADDRESS] (C.-B.Z.)  
\({}^{2}\) Industrial Engineering and Management Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania; [EMAIL_ADDRESS]  
\({}^{3}\) Department of Management Studies and Quantitative Methods, Parthenope University, 80133 Napoli, Italy; [EMAIL_ADDRESS]  
\({}^{4}\) Department of Computer Science, University of Salerno, 84084 Salerno, Italy; [EMAIL_ADDRESS]  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
**Citation:** [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON].  
of the worker. Sensors are widely used to detect body position and motion, to recognize facial expressions, and to identify objects. Such information, recorded within an assembly station, can be used to recognize the current state of the manufacturing process and to recommend possible next assembly steps. In essence, it can be said that assistive systems represent a multidisciplinary effort, involving IoT (and Industrial IoT), cognitive aspects, and Artificial Intelligence.  
In the previous works [5; 6; 7; 8], several context-based predictors were investigated. These predictors provide assembly instructions based on the current and some past assembly states. Two-level context-based predictors, Markov models and Long Short-Term Memory (LSTM), have been evaluated and the results have shown deficiencies in terms of coverage. A more detailed description of these methods is given in Section 2. As the goal is to integrate the most efficient predictor in the assembly assistance system to support the workers with choices for the next assembly step, other methods will be further investigated.  
This paper explores the predictor able to provide multiple choices for the next assembly step. By combining multiple Markov models of different orders, the Prediction by Partial Matching (PPM) can better identify manufacturing patterns. Thus, we expect that the previously mentioned assembly modeling methods will be outperformed by the PPM. For the case study, a customizable modular tablet was chosen as the target product to analyze the efficiency of the PPM in predicting the next assembly step. Prediction rate, coverage, and prediction accuracy were used as efficiency metrics. The method exploits the last assembly step(s) as input information, as well as characteristics of the human worker, such as the gender, use of eyeglasses, height, and the sleep quality in the previous night. The evaluation of the proposed method was realized using datasets collected from an experiment based on a case study, in which 68 inexperienced workers and 111 experienced factory workers performed unrestricted assemblies of the target product. Thus, the proposed predictors can learn correlations between assembly styles and characteristics of the human workers. These correlations are exploited afterwards in order to suggest the next assembly step under different conditions. The final decision support system can be very useful to inexperienced workers in an effective way, by replacing a costly training process. Even experienced workers can benefit from using it, especially in long manufacturing processes in which monotony and tiredness can otherwise lead to mistakes.  
The rest of this paper is organized as follows: Section 2 presents related work, Section 3 describes the proposed prediction methods used to recommend the next assembly step, Section 4 discusses the evaluation, whereas Section 5 concludes the paper and proposes some further work directions.