## 1 Introduction  
The most common defects during assembly include insufficient clamping force, subsequently material damage, bolt damage, loose bolts and cross threads/floating screws. Several production lines now use computerized inspection systems or operate smart factories that rely on computers for all tasks from production to management [1, 2] to prevent these defects. Despite the advantages of automated equipment, such as high productivity and reduced production costs, its widespread use across all production lines may not be feasible due to cost and the potential for defects. For delicate work, human workers may still be necessary, resulting in a hybrid assembly system that leverages the strengths of both humans and computers [3, 4]. Although fault-checking systems caused by the workers are in place in large-scale sites to mitigate these five types of defects, such systems are not prevalent in most sites and computers alone cannot prevent all such defects. Although smart factory facilities are systematically built into the assembly process, the workers may encounter challenges when executing their tasks. Specifically, the workers may find it difficult to concentrate on their work due to the opposite location of their work field and the screen displaying the work instructions. Additionally, workers who are familiar with the assembly process may deviate from the work instructions, potentially leading to defects in the manual assembly process, as described above. These problems may contribute to the occurrence of the five common defects of the manual assembly process mentioned earlier. Most repetitive assembly processes in manufacturing sites are already equipped with robots and mobile conveyor belts to increase process efficiency. However, the installation of additional robots was deemed too expensive to be feasible. Instead, a solution for increasing productivity was proposed to extract judgments, comparisons and results from workers' eye-level perspectives using a fixed camera [5; 6]. Unfortunately, finding a suitable installation area for such a camera proved difficult. As a result, it was decided to use wearable body cams to capture necessary images in real time at the worksite [7; 8]. Object-detection accuracy is essential and so the YOLO algorithm was adopted due to its high processing speed [9]. Regression analysis will be conducted using seven algorithms (Xgboost [10], Adaboost [11], Bagging [12], Extra-Trees [13], Gradient Boosting [14], Random Forest [15], Prediction Voting Regressor for Unfitted Estimators [16]) provided by the scikit-learn regression APIs that can be utilized within the Python development environment [17; 18]. This paper proposes the use of the YOLO algorithm for classification and localization to address five common fastening defects in the assembly line. The goal is to enable workers to receive real-time work instructions, conduct production inspections and receive remote support at the assembly site [19; 20]. To achieve this, we captured a working video in advance, cut it into one image per 25 frames, completed the labeling and created a weight file. We then used the weight file with a computer (which will eventually be replaced by a wearable device) to derive the position information of the worker's hand. This information was used to determine whether the tool held by the worker's hand is classified correctly, check whether a bolt is attached to the correct position and perform a comparative analysis to extract data [21; 22]. The collected data were organized into time-series data and the performance was analyzed using the aforementioned seven regression algorithms based on the location where the pin was fastened to the substrate. Based on the experimental results, we demonstrate that the proposed algorithm is effective in practical settings. A wearable device equipped with a camera that does not cause inconvenience to workers on the production line is used to film the assembly process.