# Human-Robot Interaction for Improving Fuselage Assembly Tasks: A Case Study
## 4 Results and Discussion
### Ergonomics Evaluation  
The second analysis was carried out to assess ergonomic performance in HRI configuration.  
Generally, ergonomic analysis in manufacturing production processes needs to be performed for four main factors, cause of injury due to biomechanical overload: (_i_) working postures, (_ii_) manual material handling (MMH), (_iii_) exerted forces and (_iv_) repetitive actions. In this case, only the assumed working postures contribute to biomechanical load, since there are no repetitive actions during the work shift, as well as no objects weighting more than three kilograms to handle or lift. About exerted forces, robots perform all drilling and countersinking tasks, which are the only ones for which forces are required. Thus, also exerted forces may be neglected in ergonomic analysis.  
Hence, in order to analyze working postures, Ovako Working posture Analyzing Systems (OWAS) method [45] was used. OWAS method provides risk assessment for whole body by analyzing postures assumed by human workers. It assumes four risk classes, identified by four different weights, namely 1, 2, 3, 4.  
In particular, weights for each risk class can be defined as follows:  
* Risk class 1: no risk;
* Risk class 2: low risk;
* Risk class 3: medium risk;
* Risk class 4: high risk.  
The final output of the method is the OWAS index, represented in the following Equation (4):  
\[\text{I}=[(\text{a}\times 1)+(\text{b}\times 2)+(\text{c}\times 3)+(\text{d} \times 4)]\times 100 \tag{4}\]  
where a, b, c, d are the observation frequencies for each risk class. Observation frequencies may be calculated as the number of observations for each class (\(N_{k}\)) divided the total number of observations (N):  
\[k\;=\;\frac{N_{k}}{N}\;\times 100\;with\;k\;=\;a\ldots d \tag{5}\]  
The final score is between 100 and 400. Table 6 represents the worker risk exposure based on the OWAS index and suggests possible corrective actions for high risk cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Active Time (h)** & **Passive Time (h)** & **Interaction Time (h)** & **Passive Time (\% )** \\ \hline
**Human** & 2.23 & 5.61 & 1.04 & 71.4 \\
**Robots** & 6.67 & 1.19 & 1.04 & 15.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Working time detail for human and robot in HRI configuration.  
Typically, the OWAS procedure also considers the handled weight higher that 10 kg. Since all the tools handled by human have weight less than 10 kg, the mass does not contribute to the OWAS index.  
Another important factor affecting the accuracy of OWAS analysis is the sampling time of DHM movement. In fact, a great advantage of the analysis performed by using a digital reproduction of the real workstation is the possibility to vary the sampling time in order to achieve a good tradeoff between quality of the analysis and computational time. In particular, by decreasing the sampling time, more positions are acquired and analyzed, but the processing time increases and vice versa. In this study, a good compromise was achieved by fixing sampling time at 0.05 seconds (20 Hz frequency). The processing time, considering only human active times, was of about eight hours (already scaled by SF).  
Taking into account SF, Table 7 shows the number of acquisitions falling in each action category and the observations frequency.  
OWAS index can be calculated applying Equation (4):  
\[\mathrm{I}=[(0.93885\times 1)+(0.03951\times 2)+(0.02163\times 3)+(0\times 4)] \times 100=108.278 \tag{6}\]  
The value of OWAS index falls within the low risk area; hence, there is no need for immediate corrective actions, but if possible, some organizational measures may be taken to improve the workstation. Concerning simulation, an important data are that only 7489 observations belong to action category 2, 3 or 4, corresponding to about six minutes of the human working cycle, highlighting the fact that occurring of musculo-skeletal disorders is very unlikely for this station.