Research Article

LIFTIX: Software for Evaluation of Lifting Posture Integrating Ergonomics and Machine Learning

by  Kodai Kitagawa, Kazusa Konno
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Issue 82
Published: February 2026
Authors: Kodai Kitagawa, Kazusa Konno
10.5120/ijca2026926431
PDF

Kodai Kitagawa, Kazusa Konno . LIFTIX: Software for Evaluation of Lifting Posture Integrating Ergonomics and Machine Learning. International Journal of Computer Applications. 187, 82 (February 2026), 24-28. DOI=10.5120/ijca2026926431

                        @article{ 10.5120/ijca2026926431,
                        author  = { Kodai Kitagawa,Kazusa Konno },
                        title   = { LIFTIX: Software for Evaluation of Lifting Posture Integrating Ergonomics and Machine Learning },
                        journal = { International Journal of Computer Applications },
                        year    = { 2026 },
                        volume  = { 187 },
                        number  = { 82 },
                        pages   = { 24-28 },
                        doi     = { 10.5120/ijca2026926431 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2026
                        %A Kodai Kitagawa
                        %A Kazusa Konno
                        %T LIFTIX: Software for Evaluation of Lifting Posture Integrating Ergonomics and Machine Learning%T 
                        %J International Journal of Computer Applications
                        %V 187
                        %N 82
                        %P 24-28
                        %R 10.5120/ijca2026926431
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Lumbar load in the lifting posture has been evaluated and improved to prevent lower back pain in various occupational fields, as awkward postures lead to lower back pain during manual lifting. Generally, lifting posture and lumbar load are evaluated through human observation or technical musculoskeletal simulation. Human observation has limitations in terms of repeatability and accuracy. Musculoskeletal simulators require professional skills and operational time for accurate analyses. Therefore, an easy and accurate system for evaluating lifting postures is required. Thus, the objective of this study was to develop and verify easy and accurate evaluation software for lifting postures that can be used by beginners for ergonomic posture assessment. The developed LIFTIX system calculates the compression force of L5-S1 using a combination of the Hand-Calculation Back Compressive Force Estimation Model (HCBCF) and machine learning-based pose estimation using the MediaPipe model. In addition, LIFTIX has functions for computational simulation between parameters of the biomechanical model, such as the joint angle and compression force of the L5-S1. Furthermore, these calculations and functions can be performed using a graphical user interface (GUI). In the experiment, the accuracy of the developed system, LIFTIX, was verified by comparing it with an existing ergonomic musculoskeletal simulator (3DSSPP) for 10 lifting postures. The results showed that LIFTIX could automatically evaluate the compression force of L5-S1 with a significantly high correlation (r=0.794, p<0.05) with the existing musculoskeletal simulator. These results indicate that the developed LIFTIX system can be used to evaluate lifting postures in workplaces.

References
  • E. K. Wai, D. M. Roffey, P. Bishop, B. K. Kwon, and S. Dagenais, “Causal assessment of occupational lifting and low back pain: results of a systematic review,” The Spine Journal, vol. 10, no. 6, pp. 554–566, 2010.
  • G. Waddell and A. K. Burton, “Occupational health guidelines for the management of low back pain at work: evidence review,” Occupational medicine, vol. 51, no. 2, pp. 124–135, 2001.
  • P. Coenen et al., “The effect of lifting during work on low back pain: a health impact assessment based on a meta-analysis,” Occupational and environmental medicine, vol. 71, no. 12, pp. 871–877, 2014.
  • O. Adeyemi, S. Adejuyigbe, O. Akanbi, S. Ismaila, and A. Adekoya, “Enhanced ergonomics training; a requisite to safe body postures in manual lifting tasks,” Global Journal of Researches in Industrial Engineering, vol. 13, no. 6, pp. 37–42, 2013.
  • Jp. Caneiro et al., “Evaluation of implicit associations between back posture and safety of bending and lifting in people without pain,” Scandinavian Journal of Pain, vol. 18, no. 4, pp. 719–728, 2018.
  • Y.-H. Lin, C.-S. Chen, W.-J. Chen, and C.-K. Cheng, “Characteristics of manual lifting activities in the patients with low-back pain,” International Journal of Industrial Ergonomics, vol. 29, no. 2, pp. 101–106, 2002.
  • X. Xu, C. Chang, G. S. Faber, I. Kingma, and J. T. Dennerlein, “The Validity and Interrater Reliability of Video-Based Posture Observation During Asymmetric Lifting Tasks,” Hum Factors, vol. 53, no. 4, pp. 371–382, 2011.
  • B. D. Lowe, P. Weir, and D. Andrews, “Observation-based posture assessment: review of current practice and recommendations for improvement,” 2014.
  • C. Lins, S. Fudickar, and A. Hein, “OWAS inter-rater reliability,” Applied Ergonomics, vol. 93, p. 103357, 2021.
  • R. L. Greene et al., “Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions,” Hum Factors, vol. 61, no. 1, pp. 64–77, Feb. 2019.
  • K. Kitagawa et al., “LIFTING POSTURE RECOGNITION FOR OCCUPATIONAL HEALTH USING DIGITAL IMAGE AND MACHINE LEARNING,” International Journal of Applied Biomedical Engineering, vol. 18, no. 1, pp. 1–7, 2025.
  • T. R. Waters, V. Putz-Anderson, A. Garg, and L. J. Fine, “Revised NIOSH equation for the design and evaluation of manual lifting tasks,” Ergonomics, vol. 36, no. 7, pp. 749–776, 1993.
  • M. A. Rajaee, N. Arjmand, A. Shirazi-Adl, A. Plamondon, and H. Schmidt, “Comparative evaluation of six quantitative lifting tools to estimate spine loads during static activities,” Applied ergonomics, vol. 48, pp. 22–32, 2015.
  • A. D. Hall, N. J. La Delfa, C. Loma, and J. R. Potvin, “A comparison between measured female linear arm strengths and estimates from the 3D Static Strength Prediction Program (3DSSPP),” Applied Ergonomics, vol. 94, p. 103415, 2021.
  • J. M. Willman, Beginning PyQt: A Hands-on Approach to GUI Programming with PyQt6. Berkeley, CA: Apress, 2022.
  • C. Lugaresi et al., “Mediapipe: A framework for perceiving and processing reality,” in Third workshop on computer vision for AR/VR at IEEE computer vision and pattern recognition (CVPR), 2019.
  • A. S. Merryweather, M. C. Loertscher, and D. S. Bloswick, “A revised back compressive force estimation model for ergonomic evaluation of lifting tasks,” Work, vol. 34, no. 3, pp. 263–272, 2009.
  • Y. Kanda, “Investigation of the freely available easy-to-use software ‘EZR’for medical statistics,” Bone marrow transplantation, vol. 48, no. 3, pp. 452–458, 2013.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

LIFTIX Python PyQt6 MediaPipe Compression Force of L5-S1 Hand-Calculation Back Compressive Force Estimation Model (HCBCF).

Powered by PhDFocusTM