Revolutionizing Work Posture: A Personal Journey with PoseChecker
Introduction
In recent years, technological advancements have propelled the field of motion capture into a new era, transforming the way we study human movement. Among these innovations, markerless motion capture stands out as a groundbreaking technology that has the potential to revolutionize the analysis of motion in natural settings. Unlike traditional motion capture methods that rely on markers, suits, and calibration, markerless motion capture offers a versatile approach to capturing and analyzing human motion in various environments (2). Below, I’ll share my personal experience using PoseChecker, detailing how this software evaluates ergonomic risks and provides valuable feedback to enhance posture and movements. Along the way, we’ll delve into the functionalities of PoseChecker, exploring the benefits it offers through screenshots and videos.
PoseChecker is a vision-based markerless motion capture software that uses AI-based computer vision algorithms to track body movements for ergonomic analysis. By feeding a video recording of the task into the web-based software, organizations can track and evaluate the ergonomic risks of their operations without the need for any sensors or equipment. This makes PoseChecker a user-friendly and cost-effective solution for assessing ergonomic risks.
Markerless motion capture can be contrasted with traditional methods such as marker-based motion capture, video analysis, and wearable sensors (2). While marker-based systems offer high precision, they can be cumbersome and intrusive. Video analysis may lack depth and three-dimensional accuracy (3). Wearable sensors, though portable, may have limitations in capturing complex movements. Markerless motion capture strikes a balance by combining accuracy, affordability, and scalability (4). It’s particularly useful for risk, EHS, or safety managers who need to demonstrate employee risk to leadership, as well as ergonomists responsible for corporate assessments and risk mitigation. The software can assist in diagnosing employee risk and provides quantified risk scores without requiring expert knowledge.
PoseChecker is applicable across a variety of sectors, including industrial manufacturing, construction, utilities, continuing care, operating rooms, and healthcare. Through PoseChecker, organizations can mitigate musculoskeletal disorders (MSD) across their organization using a validated, and user-friendly platform.
Understanding the Importance of Work Posture
This refers to the knowledge and awareness of how proper posture can help prevent musculoskeletal injuries in the workplace (5). Poor posture one of several MSI risk factors can lead to overworked muscles in the neck and back for example, resulting in neck and shoulder strain, back pain, or joint damage.
Adaptations and Advantages
1. Multi-Industry Postural Evaluation:
Markerless motion capture technology eliminates the need for markers to be placed on the body, making it a more convenient and accessible option for postural analysis across various environments, not limited to distinguishing between multi-subject analysis, areas of high contrast as well as when many shapes run parallel to one another (1). The tool can be used to provide feedback to workers, allowing them to adjust their posture and movements as needed.
2. Accuracy and Reliability:
Markerless motion capture technology, driven by sophisticated computer vision algorithms, offers high accuracy in capturing and analyzing human movement (1). It can track multiple joints and body parts with precision, providing detailed insights into ergonomic factors (3).
3.Affordability and Scalability:
Unlike marker-based motion capture systems that involve the placement of physical markers, markerless motion capture is more cost-effective and scalable. It eliminates the need for specialized equipment, making it accessible to a broader range of applications. The affordability and scalability of markerless motion capture contribute to it’s trend of widespread adoption in ergonomics and human factors research (3).
Enter PoseChecker: A Personal Experience
PoseChecker is a markerless motion capture software that uses artificial intelligence to analyze the ergonomics of job tasks. As a current Ergonomic Consultant, recognized by the Canadian College for the Certification of Professional Ergonomist (CCCPE), I have experienced firsthand how it can help organizations to identify and reduce the risk of musculoskeletal disorders and improve the safety and productivity of their workers. PoseChecker works by processing video recordings of the tasks and providing feedback on the posture, movement, and risk factors of the workers. The integration of AI and machine learning into the PoseChecker software empowers it with the ability to learn from and adapt to diverse situations. This continual learning process enhances their precision and effectiveness, making them increasingly efficient as they are exposed to more data over time. You can learn more about PoseChecker by visiting the EWI Works website.
1. User-Friendly Interface:
Using PoseChecker, an assessor can more confidently observe job tasks without worrying about counting out task cycles or number of times a posture occurs, as they are able to review angles and cycles later in video format. Allowing for a closer PoseChecker’s marker less motion capture stick figure overlays allowing for quick and easy identification of risky postures at a higher rate of acuity/accuracy than a trained eye.
2. Movement Analysis:
Using PoseChecker, safety programs can provide easy access to postural analysis for workers. The tool can be used to monitor workers’ movements and identify postural issues that may arise. This can help prevent injuries and improve overall safety in the workplace through the means of processing the videos through validated ergonomic tools widely used within industry, such as the Rapid Entire Body Assessment (REBA) (7) & The National Institute for Occupational Safety and Health Lifting Equations (NIOSH-LE)8. PoseChecker can start the process by populating the postural risk scores within REBA, which should then be reviewed by an assessor, thus reducing the initial calculation and tool running time. An example use case for PoseChecker could be to review job tasks through the software with joint angle limitations to aid in potential job matching cases, I.E a worker post shoulder surgery to be accommodated with modified duties after reviewing their job tasks with a limitation of upper arm angle to that of, or below 89 degree.
3. Multiple Use Cases:
Markerless motion capture systems, due to their lack of body markers required in use, have greater scalability compared to body marker-based systems. They are currently used in a wide array of industries, such as healthcare, manufacturing, and industrial sites as part of their injury prevention and rehabilitation programs.
The elimination of time loss due to attaching markers, reduction in the potential for human error or bias, and elimination of expertise required for replicable data collection pertaining to marker placement, as well as make these systems more accessible. This adaptability opens a plethora of opportunities for these systems to be integrated into various sectors and be used by traditionally non-involved stakeholders, thus implementing some aspects of participatory ergonomics.
4. Adaptability to Different Hardware:
The trend of motion analysis using video captured with a smartphone camera indicates the adaptability of markerless motion capture systems to different types of hardware. This adaptability allows for easy analysis of similar roles across multiple locations within an organization, thereby enhancing the scope of data collection and analysis. For instance, one could examine various locations within a corporation by observing the interactions of a specific job role across multiple differing workstations, thereby effectively identifying the disparities.
Markerless motion capture systems, such as the PoseChecker software paves the way for a faster, more comprehensive, and inclusive approach to motion analysis, making it a valuable tool in today’s increasingly digitally driven world / Industry 5.0.
5. Integration with AI and Machine Learning:
The integration of markerless motion capture systems with artificial intelligence and machine learning algorithms has revolutionized the field of motion analysis. These advanced technologies enable the systems to learn and adapt to various scenarios, improving their accuracy and efficiency over time. Currently through machine learning PoseChecker successfully navigates traditional markerless motion capture limitations such as occlusion, poor lighting, or multi-subject analysis.
This integration has made it possible to conduct complex analyses and generate insightful data, which can be used to optimize performance and prevent injuries in various fields, including manufacturing, healthcare, and industrial settings.
Exploring PoseChecker: Screenshots and Videos
1. Screenshot: Real-Time Posture Evaluation*
Stick figure diagram overlaid upon workers completing various task through the video-based marker less motion capture software, PoseChecker:
Conclusion
Overall, PoseChecker can be a valuable tool for safety programs looking to improve the safety and well-being of their workers. By providing easy access to markerless motion capture postural analysis, workers can be monitored and trained to maintain proper posture and avoid injury.
Markerless motion capture technology is a game-changer with transformative implications for ergonomics, design, and healthcare. Its advantages in accuracy, affordability, and scalability position it as a versatile tool with widespread applications. By providing insights into human movement, markerless motion capture enhances the design of products and systems, promotes the health and wellness of individuals, and supports the delivery of occupational health and safety services. As technology continues to evolve, the integration of markerless motion capture into various fields promises to revolutionize the way we approach ergonomics, design, and health & safety.
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References
1. EWI Works International Inc. (2021, September 28). Vision-Based Motion Capture Technology & Ergonomics. EWI Works. https://ewiworks.com/vision-based-motion-capture-technology-ergonomics
2. Remedios, S.M., Fischer, S.L. Towards the Use of 2D Video-Based Markerless Motion Capture to Measure and Parameterize Movement During Functional Capacity Evaluation. J Occup Rehabil 31, 754–767 (2021). https://doi.org/10.1007/s10926-021-10002-x Towards the Use of 2D Video-Based Markerless Motion Capture to Measure and Parameterize Movement During Functional Capacity Evaluation | SpringerLink
3. Brunner, O., Mertens, A., Nitsch, V., & Brandl, C. (2021). Accuracy of a markerless motion capture system for postural ergonomic risk assessment in occupational practice. Work (Reading, Mass.), 69(3), 731–740. https://doi.org/10.3233/WOR-219038 Accuracy of a markerless motion capture system for postural ergonomic risk assessment in occupationa (tandfonline.com)
4. Ceseracciu, E., Sawacha, Z., & Cobelli, C. (2014). Comparison of Markerless and Marker-Based Motion Capture Technologies through Simultaneous Data Collection during Gait: Proof of Concept. PLoS ONE, 9(3), e87640. https://doi.org/10.1371/journal.pone.0087640 Comparison of Markerless and Marker-Based Motion Capture Technologies through Simultaneous Data Collection during Gait: Proof of Concept | PLOS ONE
5. National Institute for Occupational Safety and Health. (1997). Musculoskeletal Disorders and Workplace Factors – A Critical Review of Epidemiologic Evidence for Work-Related Musculoskeletal Disorders of the Neck, Upper Extremity, and Low Back. Retrieved November 30, 2023, from https://stacks.cdc.gov/view/cdc/21745
6. Canadian Centre for Occupational Health and Safety. (2014. Rev. 2019). Work-related Musculoskeletal Disorders (WMSDs) – Risk Factors. Retrieved November 30, 2023, from https://www.ccohs.ca/oshanswers/diseases/wmsd/risk.html
7. Hita-Gutiérrez, M., Gómez-Galán, M., Díaz-Pérez, M., & Callejón-Ferre, Á.-J. (2020). An Overview of REBA Method Applications in the World. American Journal of Engineering and Applied Sciences, 9, 107–118. Doi: 10.3390/ijerph17082635 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215911/
8. NIOSH [1994]. Applications manual for the revised NIOSH lifting equation. By Waters TR, Ph.D., Putz–Anderson V, Ph.D., Garg A, Ph.D. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication No. 94-110 (Revised 9/2021), https://doi.org/10.26616/NIOSHPUB94110revised092021
