# Ergonomics Evaluation Using Motion Capture Technology--Literature Review
## 4 Summary of Theoretical Knowledge
According to the article **Aiding Observational Ergonomic Evaluation Methods Using MOCAP Systems Supported by AI-Based Posture Recognition**, the future of ergonomic evaluation of data collected by MoCap will be based on the application of artificial intelligence (AI). Using AI, computerised ergonomic evaluations may become more like human observation. This can help ergonomic experts with the definition of posture, which is useful when using methods such as Ovako Working Posture Assessment System (OWAS) [98]. The aim of the systematic review was also to analyse and evaluate the problems in the evaluation of ergonomics using motion capture technology and the feedback mentioned by the authors in their publications. These problems occurring during the application of motion capture technology in the workplace are discussed in several articles, which provide in their analyses improvements or compare data obtained with different technology, while highlighting their shortcomings or advantages of use. In the article **Evaluation of the Kinect\({}^{\text{TM}}\) sensor for 3-D kinematic measurement in the workplace**, the authors describe the inappropriateness of using existing motion-capturing systems for field work. They focus on the more optimal Microsoft Kinect method, comparing the obtained data with estimations from the Vicon system, and resolve the question of the feasibility, accuracy, and sensitivity of Microsoft Kinect used as a portable motion capture system at the workplace [65]. In comparison, the study **Filtered pose graph for efficient Kinect pose reconstruction** focuses on the problems of the frequently used Kinect. They highlight the high positioning requirements to obtain accurate positions. To improve the robustness of Microsoft Kinect, the authors proposed a new method for posture reconstruction based on modelling a posture database with a structure called a filtered pose graph. The study shows an improvement of the relevance of the positions and an improvement of the accuracy of the obtained data compared to the existing methods [99]. The paper **Experimental evaluation of indoor magnetic distortion effects on gait analysis performed with wearable inertial sensors** investigates the influence of magnetic fields on the distortion of outputs from a magnetic inertial measurement units (MIMU) system. Based on the gait analysis, it was found that some distortion occurs on the transverse planes of each joint and on the frontal plane of the ankle. Nevertheless, the measurements showed sufficient repeatability and the resulting data provide important information about the performance of the MIMU [100; 101]. The authors of **Detecting the Hazards of Lifting and Carrying in Construction through a Coupled 3D Sensing and IMUs Sensing System** discuss the limitations of motion measurements under extreme lighting conditions and distortion. Their research proposes the design of a connected system that integrates and synchronises Microsoft Kinect with an inertial measurement unit (IMU) that is capable of providing reliable data, even under extreme conditions [102]. Research shows that there has been significant development in modern motion capture technologies used for ergonomic analysis. Methods of detecting and compensating for errors occurring in the measurement process and new systems allow for increasingly accurate outputs and the ability to objectively assess the optimality of the working environment [76].