Predicting Hand Grip Strength of Hand Held Grass Cutter Workers: Neural Network vs Regression

Ali, M.H.; Azmir, Nor Azali; Ghazali, Mohd Imran; Yahya, Musli Nizam; Song, Jung Il

doi:10.1016/j.promfg.2015.07.077

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Vibration exposure-related handgrip strength dysfunction is a major risk factor associated with hand-arm vibration syndrome (HAVS). For all forms of vibrating devices, deterioration of handgrip power impairment may be caused by hand transmitted vibration sensitivity behaviours [38].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Effect of Handle Vibration Diesel-Fueled Single-Axle Tractor on Handgrip Strength of Operators

Busse¹,

Sinclair²,

Wondimu³

et al. 2020

Preprint

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Background: The transmission of vibration from a single-axis tractor to the human body is determined by its dynamic response; this, in turn, depends on the physical characteristics of a person's hand, the contact area, the strength of the grip, the push force of the tractor, and the position. The purpose of this work was to measure and evaluate handgrip strength and fatigue resistance for operators of a 15 horsepower single-axle tractor before and after vibration exposure. Methods: Grip strength of dominant and non-dominant hands before and after 30 minutes of tilling operations was measured and recorded. The operators performed tilling tasks with the tractor in third gear, while the vibration levels were measured at the tractor handle along with the vertical, forward, and transverse directions. Results: The average operator grip strength was 33.6 ± 2.7 and 26.3 ± 3.3 kg and the average overall grip strength declined from 39.7 to 29.31 kg, although the average fatigue strength was 27.6 and 26.5 seconds for the dominant hand before and after vibration exposure. For the non-dominant hand, the mean grip strength measured was 28.7±2.9 and 23.1±1.9 kg and the maximum grip strength was 32.79 and 26.25, while fatigue was 29.76 and 22 seconds before and after tilling respectively.Conclusion: The average reduction in grip strength suggest considerable differences in grip strength for dominant and non-dominant hands of single axle tractor operators and shows that vibration transmitted from the single axle tractor handle has a major effect on the operators.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Effect of Handle Vibration Diesel-Fueled Single-Axle Tractor on Handgrip Strength of Operators

Busse¹,

Sinclair²,

Wondimu³

et al. 2020

Preprint

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“…For instance, to analyze the hand arm vibration exposure during operation resulting in the loss of hand grip strength, 204 hand held grass cutter workers were considered. It was observed that for neural network, the performance index of regression were better fit considering the right and left hands altogether in comparison to multiple regressions, and also the neural network model was obtained more superior to the linear model (Ali et al, 2015). Mathur et al (2016) have implemented ANFIS for predicting temperature of in-socket residual limb.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

Mishra¹,

Satapathy

Jain³

2022

International Journal of Service Science, Management, Engineering, and Technology

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In the present study, 13 anthropometric hand dimensions, hand grip strength, push strength, and pull strength of 90 male farmers of Odisha in India were statistically analyzed and then, fuzzy logic toolbox of MATLAB version 2010 was used in order to create the fuzzy inference system (FIS) using ANFIS. The mean hand grip strength with standard deviation was found to be 255.21 N ± 75.46. The average push strength in standing posture for farmers was found to be 193.12 N ± 76.12, whereas pull strength in standing posture was 200.59 N ± 64.02. Very high correlation coefficient i.e. 0.977, 0.994 and 0.990 was obtained between “hand length and hand grip strength”, “hand breadth with thumb and push strength”, and “hand length and pull strength”, respectively. Finally, from the obtained ANFIS models for the prediction of muscle strength, it was concluded that ANFIS could well predict the farmers muscle strength with minimum errors. This will help to evaluate muscle capabilities to avoid musculoskeletal disorders, and in ergonomic design of tools and equipment as a health-care initiative.

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“…Após a realização destes estudos, o critério de seleção da melhor rede foi definido a partir do valor mais alto de regressão global (R²) obtido pelo sistema artificial após modificação destas configurações. Este critério de seleção é adotado por outros autores na literatura(Khademi & Behfarnia, 2016;Ali et al, 2015;Naderpour et al, 2010). Desta forma, quanto menor for a razão da soma dos quadrados dos resíduos e a soma dos quadrados total, maior será o valor do coeficiente de determinação, retratando um melhor ajuste do modelo aos dados amostrais.…”

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Modelagem Numérica Do Comportamento Mecânico De Materiais Compósitos Cimentícios Em Uma Abordagem Multiescala

SILVA¹

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Predicting Hand Grip Strength of Hand Held Grass Cutter Workers: Neural Network vs Regression

Cited by 5 publications

References 7 publications

Evaluation of Effect of Handle Vibration Diesel-Fueled Single-Axle Tractor on Handgrip Strength of Operators

Evaluation of Effect of Handle Vibration Diesel-Fueled Single-Axle Tractor on Handgrip Strength of Operators

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

Modelagem Numérica Do Comportamento Mecânico De Materiais Compósitos Cimentícios Em Uma Abordagem Multiescala

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