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Record W4389584864 · doi:10.17118/11143/20710

In-field and in-lab ergonomic assessment of manual materials handlingtasks using a passive back exoskeleton

2023· article· en· W4389584864 on OpenAlexaff
Maryam Shakourisalim, Xun Wang, Karla Beltran Martinez, Ali Golabchi, Mahdi Tavakoli, Hossein Rouhani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExoskeletonField (mathematics)Computer scienceHuman–computer interactionManual handlingMaterial handlingSimulationEngineeringManufacturing engineeringMathematics

Abstract

fetched live from OpenAlex

Abstract: There is an interest in investigating the effects of physical assistance devices such as exoskeletons in terms of reducing the risk and rate of work-related musculoskeletal disorders. Most previous studies were conducted in laboratory environments since in-field evaluations are often challenging. However, the outcomes of in-lab and in-field evaluations may not always be comparable. Moreover, workers may use some assistive tools to lift or move heavy items while holding a bending posture, leading to different risks of low back pain. Thus, we conducted an experiment to assess the differences between the in-lab and in-field levels of ergonomic risks during the manual materials handling tasks, using two different assistive tools while wearing a passive exoskeleton. For the purpose of our study, 125-lbs circular disks were lifted using the assistive tools with and without wearing a passive back-support exoskeleton (BackX, SuitX, CA,USA). Each trial took 2 repetitions and 5 seconds standing still at the beginning of each motion. The in-lab data was recorded from 10 able-bodied participants (7 males, 3 females, body mass: 61±8 kg, body height:171±48 cm, age: 23±1.5 y.o.) and the in-field data were recorded from 10 ablebodied workers (9 males, 1 female, body mass: 75±12 kg, body height:175±11 cm, age: 36±6 y.o.). We collected data using electromyography (EMG) sensors and inertial measurement units (IMUs) to record muscle activity and body posture, respectively. Furthermore, the ergonomic risk assessment was performed using the rapid entire body assessment (REBA) score. The REBA scores measured using IMU data and the max normalized EMG amplitude for each task were compared between in-field and in-lab experiments. EMG amplitude of each participant was normalized to their previously measured maximum voluntary contraction. The muscle activity measured from in-field experiments was significantly larger for most muscles and smaller for some other muscles compared to the in-lab data (p < 0.05). Muscle activities while using either tools with exoskeleton were also significantly larger for some muscles and smaller for others when the task is performed in-field compared to in-lab. In addition, the REBA score of in-field workers using both tools while wearing the exoskeleton was significantly larger than the REBA score of the in-lab participants. The results of this study suggest the need for ergonomic risk assessment and occupational exoskeleton evaluation in real-world environments in addition to lab assessments. The sources of this difference between the in-lab and in-field results could be 1) potential differences between the task implementation in the two environments, 2) the lack of experience of in-lab participants compared to in-field workers and 3) the inconsistency of the male-to-female participants ratios between lab and field experiments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.280
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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