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Efficiency assessment of passive exoskeletons for manual handling tasks

  • Author / Creator
    Wang, Xun
  • Work-related musculoskeletal disorders (WMSDs) have been reported as a major issue among construction workers, and governments have spent billions of dollars on the problem. However, the number of case reports is still growing steadily year by year. Multiple factors can lead to WMSDs, but the two main ones are awkward body posture during work and unsafe workloads on specific parts of the body. In total, the factors that produce unsafe joint moments will have a high risk of leading to WMSDs. Currently, the most commonly used methods to assess ergonomic risks in real-world scenarios are scoring methods based on body posture, including rapid upper limb assessment (RULA), rapid entire body assessment (REBA), and Ovako working posture analysis system (OWAS).
    This thesis investigated the ergonomic risks of WMSDs among construction workers in drainage departments who need to deal with manhole covers, and the efficiency of the passive trunk-support exoskeleton for manhole cover lifting tasks with i) manual lifting, ii) lifting with a jake tool, and iii) lifting with a lever tool. To assess the risk from the factors that lead to WMSDs, muscle activity and body posture were both measured during the in-field trial. For data collection, we performed an in-field experiment on 20 able-bodied construction workers from drainage management departments. During the experiment, the workers used the jake tool and lever tool to lift a 125 lb manhole cover and lifted a 40 lb manhole cover manually. Meanwhile, a passive exoskeleton with two modes, standard mode and instant mode, was applied to the above-mentioned working scenarios. Data on muscle activity was collected by the surface electromyogram (sEMG) sensors and normalized by maximum voluntary contraction (MVC). Data on body posture was collected by the inertial measurement unit (IMU) and presented as REBA scores. Also, questionnaires were used to collect the participants’ feedback on the difficulty of the tasks and confidence in the exoskeleton.
    Furthermore, we observed the issue that the sEMG signal will sometimes be larger than the MVC signal collected by conventional methods during the in-field tests, especially for low back muscles. To fix the problem, we proposed a new dynamic MVC method for low back muscles which is more valid for dynamic tasks. Inspired by the working scenario at the time point when the problem occurred, we designed the dynamic MVC collecting procedure, which let the participants lift a 45 lb weight while standing with elbow and knee joints locked, using only the trunk. Also, to collect the maximum signal, external force against the lifting direction was applied, and sEMG data for low back muscles were collected. To investigate the validity of the methods, a duplicated test similar to the in-field test was carried out and normalized by the dynamic MVC method and four other conventional MVC methods for the low back. Finally, we found that the sEMG data normalized by the dynamic MVC method is significantly lower than the data normalized by conventional methods (P-values for Wilcoxon signed-rank test < 0.05) and all the data less than 100%, which proved the validity of the dynamic MVC method.
    After fixing the problem, ergonomic risk assessment tests were carried out. The normalized sEMG data and REBA scores for different situations were compared and tested by the Wilcoxon signed-rank test. We observed that the lever tool performed best in the manhole cover lifting task, with significantly lower muscle load on the low back, shoulders, and lower limbs, and lower REBA scores than the data for the task using the jake tool method (P-values for Wilcoxon signed-rank test < 0.05). Meanwhile, the efficiency of the passive exoskeleton varied with different body postures; tasks using the jake and lever tools (especially for lower back and lower limb) had good efficiency, while the manual-only task had lower efficiency.
    In summary, this thesis proposed a novel dynamic MVC method, assessed the ergonomic risk for manhole cover lifting tasks and provided specific recommendations for the tasks based on the results.

  • Subjects / Keywords
  • Graduation date
    Spring 2023
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-wcrj-9961
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.