Fatigue recognition in overhead assembly based on a soft robotic exosuit for worker assistance

Kuschan, Jan; Krüger, Jörg

doi:10.1016/j.cirp.2021.04.034

Cited by 20 publications

(5 citation statements)

References 9 publications

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“…They then applied a Machine Learning (ML) algorithm, specifically a Support Vector Machine (SVM) model, to classify the fatigue levels of subjects. This methodology was similarly adopted in studies by Zhang et al, (2013), Baghdadi et al, (2018), and Kuschan and Krüger (2021), who also used SVM models for physical fatigue detection [41,42,43]. 2017) to conduct fatigue prediction for manual material handling tasks [44].…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Measurement of Employee Stress and Fatigue in Artificial Intelligence-Driven Workforce Management Environments: An Empirical Study Using Aicoach

Adam Dalnoki,

Md Iftekhar Islam

2023

EPRA

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Effective fatigue management in the workplace is associated with employee well-being, safety, and productivity. Fatigue, often resulting from inadequate rest and high workloads, can lead to decreased cognitive function, diminished job performance, and increased risk of accidents, thus affecting both individual health and organizational efficiency. For this, we introduced an innovative approach to understanding and addressing workplace fatigue. By analyzing the interrelationships between employees self-reported fatigue levels, their perceptions of managing fatigue, and their engagement with an AI-based coaching tool (AiCoach), we sought to uncover patterns that could inform more effective fatigue management strategies. Using advanced sentiment analysis adapted for the context of fatigue and Granger-causality tests, we examined these dynamics over time. Our findings highlight the importance of immediate perceptions of fatigue in predicting engagement with management interventions, underscoring the need for real-time monitoring and adaptive strategies in managing workplace fatigue. KEYWORDS: Workplace Fatigue, Stress Management, AiCoach, Sentiment Analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Digital Measurement of Employee Stress and Fatigue in Artificial Intelligence-Driven Workforce Management Environments: An Empirical Study Using Aicoach

Adam Dalnoki,

Md Iftekhar Islam

2023

EPRA

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“…After developing a flexible driving structure named “18 Weave” with thin McKibben muscle, Abe et al [ 46 ] constructed an exosuit based on it to support upper limb movements such as shoulder flexion and elbow flexion. The PowerGrasp [ 47 ] belonged to a modular pneumatic system which adjusted the chamber’s stiffness to support shoulder, elbow, and wrist/finger during assembly. Authors have carried out research on automated fatigue detection and classification to give advice on rest or to adjust support for workers.…”

Section: Overview Of Exosuitmentioning

confidence: 99%

“…Industrial production: Workers can perform manual labor such as lifting, carrying and assembling more efficiently with the help of exosuits [ 133 ]. They possess the ability to relieve physical fatigue and improve working efficiency [ 47 ]. Besides, some illnesses and injuries associated with overstrain could be prevented if proper assistance is provided.…”

Section: Applications and Challengesmentioning

confidence: 99%

Soft Wearable Robots: Development Status and Technical Challenges

Shi

Dong

Lin

et al. 2022

Sensors

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In recent years, more and more research has begun to focus on the flexible and lightweight design of wearable robots. During this process, many novel concepts and achievements have been continuously made and shown to the public, while new problems have emerged at the same time, which need to be solved. In this paper, we give an overview of the development status of soft wearable robots for human movement assistance. On the basis of a clear definition, we perform a system classification according to the target assisted joint and attempt to describe the overall prototype design level in related fields. Additionally, it is necessary to sort out the latest research progress of key technologies such as structure, actuation, control and evaluation, thereby analyzing the design ideas and basic characteristics of them. Finally, we discuss the possible application fields, and propose the main challenges of this valuable research direction.

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“…However, the major benefit of this method is that there are several measures devised for monitoring patient performance and providing individualized feedback, which are crucial for tailoring the therapy . New advancements have enabled the development of new robotics measures specifically for wrist rehabilitation [8]- [10]. These devices are equipped with the required sensors, actuators, and automatically controlled algorithms to deliver recovery.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and IoT Based Robotic Solution for Home Wrist Rehabilitation through Motion Capture Enabled Architecture

Ch. Gangadhar

2024

jes

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The present study investigates the effectiveness of an integrated approach that combines machine learning, robotic rehabilitation, and traditional methods for wrist rehabilitation among the hemiparesis population. Subjects with robotic rehabilitation at the four-week intervention period showed the ball’s significant difference in the flexion and extension angles and demonstrated the better overall outcomes from the robotic intervention . The machine learning models, in particular, the artificial neural networks, provided the prediction of rehabilitation outcomes and demonstrated the high accuracy, high precision, high recall, and high F1 score. This study supports the idea of the beneficial effects of combining robotic rehabilitation with machine learning algorithms and supports the notion that the two benefits could bring high synergistic effects above their individual potentials. The report also contributes to the development of the rehabilitation science of wrists, given that few studies have explored the applications of artificial intelligence methods or robotic rehabilitation beyond the context of feasibility reports. The findings of the current research could contribute useful insights for the decision-making practices of therapists and healthcare providers, also given the reliance on large data contributed by the previous interventions. Future study could investigate the long-term results, the cost-effectiveness of the approaches, and assist in developing the potential results of customization for individual patients.

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Fatigue recognition in overhead assembly based on a soft robotic exosuit for worker assistance

Cited by 20 publications

References 9 publications

Digital Measurement of Employee Stress and Fatigue in Artificial Intelligence-Driven Workforce Management Environments: An Empirical Study Using Aicoach

Digital Measurement of Employee Stress and Fatigue in Artificial Intelligence-Driven Workforce Management Environments: An Empirical Study Using Aicoach

Soft Wearable Robots: Development Status and Technical Challenges

Machine learning and IoT Based Robotic Solution for Home Wrist Rehabilitation through Motion Capture Enabled Architecture

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