# Ergonomic Assessment of Physical Load in Slovak Industry Using Wearable Technologies
## 2 Materials and Methods
### Measurement Tools  
To assess the ergonomic risk, the wireless sensor ergonomic system TEA Captiv was used simultaneously with another wearable system--smartwatch Actigraph. Captiv was chosen due to its strong features and availability of a multimodal and complex human motion analysis. [PERSON] et al. [42] compared the accuracy of motion capture for complex movements using the Captiv system with Microsoft Kinect V2 and considered Captiv preferable for ergonomic analyses in terms of accuracy in the majority of cases. This was especially the case in industrial work environments with occlusions. [PERSON] et al. 2019 [43] demonstrated the accuracy, reliability of IMU (inertial measurement units) for outdoor \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**N** & **Gender** & **Age** & **Weight [kg]** & **Height [m]** & **BMI** \\ \hline Participant 1 & M & 43 & 81 & 1.68 & 28.70 \\ Participant 2 & M & 59 & 90 & 1.85 & 26.30 \\ Participant 3 & M & 60 & 82 & 1.78 & 25.88 \\ Participant 4 & F & 44 & 85 & 1.68 & 30.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Anthropometric parameters of participated subjects consisted of 3 males and females, age group 40–49, 50–59, and 60+. BMI—Body Mass Index is a measure of body fat based on height and weight that applies to adult men and women. Figure 1: Lower body exoskeleton—Chairless Chair 2.0 (CC 2.0). Exoskeleton was used in Experiment 2 (EXO): minimizing the physical load by ergonomic sit-stand pattern. motion capturing in diverse activities (regular tasks such as walking and fast, complicated tasks such as rehabilitation and sports). Captiv enables an adaptable and scalable solution for capturing workers in their work environments thanks to a multifunctional analysis embodying body posture, carrying capacity, musculoskeletal limitations, and repetitive movements and vibrations. It is a flexible, scalable measurement and analysis toolkit for ergonomics, workplace analysis, occupational safety, HMI (Human-Machine Interface), prototyping, research, VR (Virtual Reality), and more applications [39]. Simple software enables a quick analysis of tasks based on a video, up to a complex multimodal measuring system with synchronous video record. Wireless motion sensors use inertial measurement units (IMU) and16-channel T-Rec wireless receivers with a range of 15 m in real-time. Measured joints, in accordance with thresholds for appropriate range of motion in our experiments were (Table 2): Neck, Back (axis: pelvis--vertebral segment T2), Left shoulder, Right shoulder, Left hip (axis: pelvis--upper left leg), and Right hip (axis: pelvis--upper right leg). For correct 3D visualization, the subject was equipped with a sensor on his back (for tracking the upper body) and on his hip (for tracking the lower body). The measured data were displayed via a 3D avatar (virtual human mannequin), which offered animations of the provided task with visualizations of system evaluation results by marking body segments with green/orange/red colors, indicating fully customizable threshold values for reference angles. Green color means suitable conditions for a segment loading; orange indicates a change in activity that has to be considered, and red indicates inappropriate activity that needs an immediate correction. Figure 2a shows the placement of 7 motion sensors (MO) on the following segments: Head (forehead), Back (spine on T2), Pelvis, Left and right arms (humerus), Left and right forearms (radius, cubitus), and Upper left and right legs (femur). The [PERSON]'s avatar represents worker activity, and his simultaneous joint angles indicate with color which threshold values exceeded in the monitored joints, see Figure 2a,b and Figure 3a,b. Measuring multiple biological signals using different types of sensors can significantly improve the accuracy of physical activity parameter estimates, as opposed to measuring only one signal.