Impact of Visual Biofeedback of Trunk Sway Smoothness on Motor Learning during Unipedal Stance

Cruz‐Montecinos, Carlos; Cuesta‐Vargas, Antonio; Muñoz, Cristian; Flores, Dante; Ellsworth, Joseph; Fuente, Carlos de la; Calatayud, Joaquín; Rivera-Lillo, Gonzalo; Soto-Arellano, Verónica; Tapia, Claudio; García‐Massó, Xavier

doi:10.3390/s20092585

Cited by 7 publications

(8 citation statements)

References 59 publications

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“…Subsequently, Spearman's rho (ρ) correlation coefficient was used to compare the correlation of the two systems for each posture. To verify hypothesis 2, the data from the three experts were used to calculate the intra-class correlation coefficient (ICC) for evaluating the reliability coefficient among observers [45]. The Quick Capture's REBA score results were also used to calculate ICC.…”

Section: Discussionmentioning

confidence: 99%

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An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders

Zhang

Lee

et al. 2020

Sensors

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Determining the potential risks of musculoskeletal disorders through working postures in a workplace is expensive and time-consuming. A novel intelligent rapid entire body assessment (REBA) system based on convolutional pose machines (CPM), entitled the Quick Capture system, was applied to determine the risk levels. The aim of the study was to validate the feasibility and reliability of the CPM-based REBA system through a simulation experiment. The reliability was calculated from the differences of motion angles between the CPM-based REBA and a motion capture system. Results show the data collected by the Quick Capture system were consistent with those of the motion capture system; the average of root mean squared error (RMSE) was 4.77 and the average of Spearman’s rho (ρ) correlation coefficient in the different 12 postures was 0.915. For feasibility evaluation, the linear weighted Cohen’s kappa between the REBA score obtained by the Quick Capture system and those from the three experts were used. The result shows good agreement, with an average proportion agreement index (P0) of 0.952 and kappa of 0.738. The Quick Capture system does not only accurately analyze working posture, but also accurately determines risk level of musculoskeletal disorders. This study suggested that the Quick Capture system could be applied for a rapid and real-time on-site assessment.

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Section: Discussionmentioning

confidence: 99%

“…To verify hypothesis 2, the data from the three experts were used to calculate the intra-class correlation coefficient (ICC) for evaluating the reliability coefficient among observers [ 45 ]. The Quick Capture’s REBA score results were also used to calculate ICC.…”

Section: Methodsmentioning

confidence: 99%

An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders

Zhang

Lee

et al. 2020

Sensors

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“…With the development of artificial intelligence technology, Cruz-Montecinos et al have proposed the integration of computer image recognition technology with self-reports, questionnaires, and observational methods to improve assessment efficiency and accuracy [28]. However, different implementation methods of technological assessment have fundamental differences in the human skeleton recognition module and are typically divided into two categories: one involves hardware-assisted recognition, and the other relies solely on algorithms, such as convolutional neural networks and convolutional pose machines.…”

Section: Examples Of Technologies To Support Ergonomic Assessmentsmentioning

confidence: 99%

A Quick Capture Evaluation System for the Automatic Assessment of Work-Related Musculoskeletal Disorders for Sanitation Workers

Zhang,

Huang

2024

Applied Sciences

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Musculoskeletal disorders not only impact workers’ health but also result in significant economic losses to society. Sanitation workers often have to lift waste bags from containers, leading to shoulder joint flexion of 90° or more, exposing them to hazardous environments for extended periods. This study combines deep learning and image recognition to create a Quick Capture Evaluation System (QCES). By comparing body angles captured in the sanitation workers’ work environment with those from OptiTrack motion capture, the system showed an average Root Mean Square Error of 5.64 for 18 different postures, and an average Spearman’s rho of 0.87, indicating its precision. Compared with scores assessed by three experts, the system demonstrated an average Cohen’s kappa of 0.766, proving its reliability. Practical assessments of sanitation workers revealed that tilting the waste containers could significantly improve their posture and reduce the risk of Work-Related Musculoskeletal Disorders. It proves that the QCES system can accurately and rapidly assess the on-site posture of a particular occupation.

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“…Postural training increased the ability to maintain balance under challenging conditions, such as tandem [ 7 ] and unipedal stance [ 7 – 11 ], leading to a reduction of trunk acceleration [ 12 ] and of the area covered by the Centre of Pressure (CoP) [ 1 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of a short period of postural training on postural stability and vestibulospinal reflexes

et al. 2023

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The effects of postural training on postural stability and vestibulospinal reflexes (VSRs) were investigated in normal subjects. A period (23 minutes) of repeated episodes (n = 10, 50 seconds) of unipedal stance elicited a progressive reduction of the area covered by centre of pressure (CoP) displacement, of average CoP displacement along the X and Y axes and of CoP velocity observed in this challenging postural task. All these changes were correlated to each other with the only exception of those in X and Y CoP displacement. Moreover, they were larger in the subjects showing higher initial instability in unipedal stance, suggesting that they were triggered by the modulation of sensory afferents signalling body sway. No changes in bipedal stance occurred soon and 1 hour after this period of postural training, while a reduction of CoP displacement was apparent after 24 hours, possibly due to a beneficial effect of overnight sleep on postural learning. The same period of postural training also reduced the CoP displacement elicited by electrical vestibular stimulation (EVS) along the X axis up to 24 hours following the training end. No significant changes in postural parameters of bipedal stance and VSRs could be observed in control experiments where subjects were tested at identical time points without performing the postural training. Therefore, postural training led to a stricter control of CoP displacement, possibly acting through the cerebellum by enhancing feedforward mechanisms of postural stability and by depressing the VSR, the most important reflex mechanism involved in balance maintenance under challenging conditions.

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Impact of Visual Biofeedback of Trunk Sway Smoothness on Motor Learning during Unipedal Stance

Cited by 7 publications

References 59 publications

An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders

An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders

A Quick Capture Evaluation System for the Automatic Assessment of Work-Related Musculoskeletal Disorders for Sanitation Workers

Effects of a short period of postural training on postural stability and vestibulospinal reflexes

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