Industry Perception of the Suitability of Wearable Robot for Construction Work

Gonsalves, Nihar; Akanmu, Abiola; Gao, Xinghua; Agee, Philip; Shojaei, Alireza

doi:10.1061/jcemd4.coeng-12762

Cited by 18 publications

(7 citation statements)

References 52 publications

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“…Chen et al (2016) opined that when a person places too much attention on a task, the individual has less attention to focus on other stimuli. In the context of this study, exoskeleton-use may demand attention, thereby reducing the mental resources that may be required for the task or being aware of the user's surrounding to prevent fall risks, and catch and snag risks (Gonsalves et al, 2023;Zhu et al, 2021). These risks can impact the mental workload resulting in exoskeleton users being stressed or distracted, thus retarding their productivity and safety.…”

Section: Mental Workload Evaluation With Eegmentioning

confidence: 99%

“…Exoskeletons are increasingly being perceived as a solution to WMSDs. Exoskeletons, such as back-support exoskeletons, are wearable devices designed to support or augment users' back while performing work (Gonsalves et al, 2023;Ogunseiju et al, 2022). Exoskeletons are classified as passive and active depending on their mode of augmentation.…”

Section: Introductionmentioning

confidence: 99%

“…These devices have been shown to reduce risks factors of back injuries by reducing muscle activity (Theurel et al, 2018), range of motion (Cumplido-Trasmonte et al, 2023), body discomfort (Gonsalves et al, 2021;Kim et al, 2019), and rate of exertion (Alemi et al, 2020;Baltrusch et al, 2021). Despite these benefits, studies have shown that exoskeleton-use in construction can trigger difficulty working in confined spaces (Nussbaum et al, 2019), fall risks due to the weight of the device (Alabdulkarim et al, 2019;Kim et al, 2019;Massardi et al, 2023), discomfort to body parts (Gonsalves et al, 2023;Gonsalves et al, 2021), restrictions in movement (Fox et al, 2020;Poliero et al, 2020), catch and snag risks (de Looze et al, 2016;Kim et al, 2019), and thermal discomfort (Liu et al, 2021). The devices could also be challenging to adjust to fit (Gonsalves et al, 2023;Gorgey, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Despite these benefits, studies have shown that exoskeleton-use in construction can trigger difficulty working in confined spaces (Nussbaum et al, 2019), fall risks due to the weight of the device (Alabdulkarim et al, 2019;Kim et al, 2019;Massardi et al, 2023), discomfort to body parts (Gonsalves et al, 2023;Gonsalves et al, 2021), restrictions in movement (Fox et al, 2020;Poliero et al, 2020), catch and snag risks (de Looze et al, 2016;Kim et al, 2019), and thermal discomfort (Liu et al, 2021). The devices could also be challenging to adjust to fit (Gonsalves et al, 2023;Gorgey, 2018). Moreover, unequal loading and balancing of body parts due to improper adjustment can cause users to be more aware of the device than their task and surrounding, which could increase workers' mental workload (Bequette et al, 2020;Marchand et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach

Akanmu,

Afolabi,

Okunola

2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

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Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using active back-support exoskeletons for construction work. Laboratory experiments were conducted wherein Electroencephalography (EEG) data were collected from participants wearing active back-support exoskeletons to perform flooring task. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory network was trained to forecast future time steps of the processed EEG data. The performance of the network was evaluated using root mean square error (RMSE) and r-squared (R2). A RMSE of 0.1527 and R2 of 0.9665 indicating good fit and strong correlation, respectively, were observed between the predicted and actual EEG data. Results of the comparison between the actual and predicted mental workload also show strong correction with an R2 of 0.8692. The findings motivate research directions into real-time monitoring of mental workload of workers during exoskeleton-use. The study has significant implications for stakeholders, enabling them to gain a deeper understanding of the impact of mental workload while using exoskeletons thereby providing opportunities for mitigation

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Section: Mental Workload Evaluation With Eegmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach

Akanmu,

Afolabi,

Okunola

2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

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“…Industrial workers worldwide are increasingly affected by these disorders due to repetitive tasks and heavy lifting (Sebbag et al, 2019). These physical and environmental factors can put stress on workers' musculoskeletal systems, including muscles, joints, tendons, ligaments and nerves, which can lead to musculoskeletal disorders, increasing the need to develop wearable robots that can help industrial workers (Gonsalves et al, 2023). To improve work performance, researchers focus on developing rigid-based wearable robots called exoskeleton robots.…”

Section: Introductionmentioning

confidence: 99%

Enhancing wearability: designing wearable suit platforms for industrial workers

Chung,

Won,

Lee

et al. 2024

IJCST

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PurposeThe objective of this study was to develop wearable suit platforms with various anchoring structure designs with the intention of improving wearability and enhancing user satisfaction.Design/methodology/approachThis study selected fabrics and materials for the suit platform through material performance tests. Two anchoring structure designs, 11-type and X-type are compared with regular clothing under control conditions. To evaluate the comfort level of the wearable suit platform, a satisfaction survey and electroencephalogram (EEG) measurements are conducted to triangulate the findings.FindingsThe 11-type exhibited higher values in comfort indicators such as α, θ, α/High-β and lower values in concentration or stress indicators such as β, ϒ, sensorimotor rhythm (SMR)+Mid-β/θ, and a spectral edge frequency of 95% compared to the X-type while walking. The 11-type offers greater comfort and satisfaction compared to the X-type when lifting based on the EEG measurements and the participants survey.Originality/valueIt is recommended to implement the 11-type when designing wearable suit platforms. These findings offer essential data on wearability, which can guide the development of soft wearable robots.

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Soft Upper‐Limb Wearable Robotic Devices: Technology and Applications

Sharma,

Phan,

Davies

et al. 2024

Advanced Intelligent Systems

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One of the practical applications in the field of soft robotics involves the development of soft robotic wearable devices. These devices make use of their intrinsically compliant structures to interact safely and harmoniously with the human body. While soft wearable robots demonstrate their utility in lower‐limb applications for locomotion, the upper‐limb domain offers significant prospects in a wide range of applications that soft robotic technology can address. In this review, the current state of technology in the field of soft wearable upper limbs is systematically analyzed and categorized. Categorizations are made based on their applications in rehabilitation, activities of daily living support, and human augmentation. Furthermore, in this study, also contemporary technological aspects, encompassing sensing technology and control systems, are explored. Despite exciting potential in this domain, several limitations from existing devices inherently impede widespread adoption and thus hinder further progress in the field. In this study, also an overview of the different facets of the domain is provided and key considerations for the advancement of soft wearable robotic devices intended for upper‐limb applications are prescribed.

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Industry Perception of the Suitability of Wearable Robot for Construction Work

Cited by 18 publications

References 52 publications

Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach

Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach

Enhancing wearability: designing wearable suit platforms for industrial workers

Soft Upper‐Limb Wearable Robotic Devices: Technology and Applications

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