Consequently, individuals may experience health side effects associated with prolonged use of computers due to maintaining poor postures and poor workspace ergonomics. In addition, this study also includes the role of computer vision technology as well as machine learning that can be integrated together in a computer application to assist in the detection of poor workspace ergonomics and poor posture to further prevent or reduce the extent of such effects. The developed prototype with posture and workspace ergonomics detection capabilities has been proven to demonstrate that computer vision can effectively detect human posture as well as improve one's workspace.
I was able to receive thorough feedback on my project and guide me to do the right things and gave me approval to continue my final year project on the chosen topic of 'Workspace Ergonomics and Perception of Postural Effectiveness Using Computer Vision with a Machine Learning Approach'.
INTRODUCTION
- Background
- Problem Statement
- Objectives
- Scope of Study
Thus, computer vision can also be applied in detecting one's posture and associated workspace items to evaluate the effectiveness of workspace ergonomics and posture. In the long run, poor workplace ergonomics and poor posture can have negative effects that progressively affect the health and quality of life of individuals. To develop a software capable of detecting individual posture and the effectiveness of workspace ergonomics through the implementation of machine learning and computer vision technologies.
The purpose of this project will include studying the negative health effects of workplace ergonomics and poor posture by conducting assessments and surveys on university students aged 20 to 23 years.
LITERATURE REVIEW AND/OR THEORY
- Emergence of Computer Use
- Negative Health Effects Associated with Poor Workspace Ergonomics
- Causes of Poor Posture
- Health Effects of Prolonged Poor Postures
- Solution to Poor Workspace Ergonomics
- Optimal Viewing Distance
- Computer Vision and Convolutional Neural Network Algorithm
- Applying Machine Learning together with Computer Vision
- History of Monitoring Postures
- Application of Computer Vision for Posture Detection & Related Work
- OpenPose and MediaPipe Library Comparison
As the neck is pressed, it also leads to adjustments of the position of the upper back and lower back. Hecht (2020) also concluded that bad posture can even lead to degeneration of the muscles and the joints. The height of the seat pan must be adjusted so that the feet of the person sitting lie flat on the surface of the ground.
The optimal viewing distance refers to the ideal distance between the individual and the computer screen. In comparison, my project focuses on the side view angle where the curvature of the spinal posture can be easily observed which can effectively assess the individual's posture. The frames per second can be seen in the white text in the upper left corner of the diagrams.
METHODOLOGY/PROJECT WORK
Introduction
Agile Methodology
- Requirements Phase
- Design Phase
- Development Phase
- Test Phase
The requirements phase involves gathering all the necessary requirements before developing the software itself. The design phases include the production of interface models for the software and integrate all identified requirements into the designs. In this case, the software that is planned to be developed involves the use of the Python programming language as it is the main language involved in machine learning projects.
The software that will be used for the software development is PyCharm for the main software functions that include computer vision and machine learning and PyQt5 designer for the front-end. This involves applying the designs as specified in the design phase and performing the coding to develop the software itself. Since the development of the software involves the integration of computer vision, there are specific procedures that must be followed to effectively engineer the deliverables in.
After the processes are done, the system will first be able to measure the angular orientation based on the orientation of the bounding box. Finally, the implementation is the finished delivery, where the final product can be used. The testing phase requires testing the developed software to ensure that the software is working as intended.
This phase is mandatory before the software release as it will identify any potential issues or mistakes made that were overlooked during the design phase. Unit testing, system testing, integration testing and acceptance testing are the various tests that can be done to determine that any problems persist within the software.
Testing Methodology
- Posture Test
- Eye Distance Test
The camera will be placed 160 centimeters away from the chair and will be placed at an angle of 90°. This provides sufficient distance for the software to detect and capture the person's entire body for posture assessment. A pre-assessment survey will help to better understand the participant by understanding their background and determine if the participant is symptom-free prior to the experiment.
The post-assessment survey involves finding out if the person has observed any health symptoms which requires the person to rate the level of severity of the general symptoms based on a Likert scale ranging from 1 to 5 where 1 represents no stress and 5 represents high stress. For this experiment, the hypothesis is to find out whether the person who uses the computer for 3 hours will have more chances to get any symptoms related to computer vision syndrome. The first test will involve the person using their computer at an appropriate distance of 50 or more centimeters per OSHA requirements.
The second test requires the person to use their computer at a much closer distance of less than 50 centimeters. Variables in particular, the setup, position of the laptop, lighting and seating position are fixed at all times, while the independent variable is the distance from the person looking at the laptop screen and will change depending on the type of test the person is doing. The setting of this experiment requires a chair to be placed directly in front of the camera and the subject interacting with the laptop.
The laptop screen will be tilted at a 90° angle so it does not affect the distance estimation from the software. Similar to the posture experiment, the participant will be administered a survey at the end of the experiment to gather additional information about the person's background and to identify any symptoms associated with computer vision syndrome.
RESULTS AND DISCUSSIONS
Software
- Posture Detection
- Face Distance Estimation
- Workspace Objects’ Angle Orientation
The second function of this software is to estimate the distance between the person and the laptop screen. The purpose of this is to monitor a person's sitting distance to ensure that the person is not sitting close to the laptop's screen, as this can affect the individual's eye and cause computer vision syndromes. Since it is relatively difficult to detect eyes compared to faces due to its smaller size, measuring the distance between the screen and the face is another solution to estimate the distance between the person's eyes.
To make accurate estimates, the system undertook a supervised learning process that provided a reference image containing a person's face and the width of the person's face. This helps determine the focal length of the camera, which helps achieve face distance estimates. As shown in Figure 4.1.1, the person can be seen sitting about 53.37 centimeters from the laptop's screen, resulting in an optimal distance.
To assess the accuracy of the facial distance estimation, a comparison was made to observe what the software had estimated, showing that the person sitting was 56.65 centimeters as perceived by the software shown in Figure 4.1. 2.3, but in reality the person was actually sitting between 54 and 56 centimeters as shown in figure 4.1.2.4. A test was further conducted to measure the accuracy of the face distance estimation by having a person sit exactly 60 centimeters away from the screen and compare with the system's input data for a period of 60 seconds. Further calculations to arrive at the accuracy of the face distance estimation involve the use of standard deviation and the calculation of the margin of error.
For example, in the office environment, if the seat the person is sitting in is not reclined properly, this can potentially affect the way the person sits, which can affect their body posture. This operation is performed by utilizing the built-in functions of computer vision to find the contours of the target object that meet the specified thresholds and proceed to group them together and place a bounding box surrounding the contours of the object.
Experiment
The second participant had maintained good posture 99.13% of the time for the first test and poor posture 98.6% of the time for the second test. According to the study, it was found that participants who maintained good posture for 3 hours did not experience back pain or other posture-related symptoms, while participants who maintained poor posture experienced back pain. For the face distance experiment, the first participant consistently maintained good distance 94.49% of the time for the first test and poor distance 99.88% of the time for the second test, as shown in Figure 4.2.3.
For Participant 2, the participant consistently maintained good distance 100% of the time for the first test and poor distance 100% of the time for the second test, as shown in Figure 4.2.4. Based on the results of the study, participants who underwent the optimal distance test did not experience eye strain, but the first participant did experience mild dry eyes. First of all, positioning the system to monitor the posture of the individual in the side view angle is one of the effective ways to observe postures.
To elaborate further, the system can correctly detect a person's position on one side being exposed, but on the other side the person will not be visible from the camera because the side that was exposed covers the other side. Therefore, it can gradually affect how the system interprets the person's posture and potentially derive inaccurate posture classification. For example, the system might add the key point on a person's leg, but it was incorrectly placed somewhere else.
This is due to a misinterpretation of the system and this limitation occurs due to a poor quality camera without the ability to perceive depth, the presence of objects that obstruct the view of the camera, the color of the person's clothing during the experiment and even environmental factors that include poor lighting and indistinct backgrounds. Third, it is difficult to gain insight and effectively prove a hypothesis based on experiment.
CONCLUSION AND RECOMMENDATIONS
Conclusion
Recommendations
