Measuring anthropometric data for designing hand tools in handicraft industries

Meen, M.L.; Dangayach, G.S.; Bhardwaj, Arvind Kumar

doi:10.1504/ijpmb.2013.058159

Cited by 13 publications

(3 citation statements)

References 27 publications

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“…However, literatures reported various musculoskeletal disorders (MSDs) that have an effect on complete body cells/tissues including nerves and tendon sheaths, in work-system design not considering ergonomics principles (Jaffar et al, 2011). These studies (Choobineh et al, 2007a;Hallbeck, 2010;Nazari et al, 2012;Mahmoudi and Bazrafshan, 2013;Meena et al, 2013;Metgud et al, 2008;Motamedzade et al, 2007;Motamedzade and Afshari, 2014) shows vital role played by ergonomics science while designing work-place across industries. To describe the concept of ergonomics, numerous researchers have given a variety of definitions which are given in Table 1.…”

Section: Introductionmentioning

confidence: 99%

A Literature Review of Musculoskeletal Disorders in Handicraft Sector

Meena

Dangayach

Bhardwaj

2016

International Journal of Applied Industrial Engineering

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This research review article made an attempt to review such issues pertaining to handicraft sector. It highlight/covers following aspects in detail: ergonomics interventions in handicraft industries, occupational risk factor, and musculoskeletal disorders. The literatures over a decade reported that ergonomics concepts are more applied in large-scale industries like steel plant, power plant, manufacturing plants, automotive sectors etc. rather than small-scale industries like handicraft. However, applying ergonomics concepts/principles in such industries would definitely lead to increase in the work-system-worker productivity by improvement in tools, methods, work-environment, minimizing injuries and disorders. Based on literature studies recommendations are made that significant lead to improvement in productivity of such industries.

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Section: Introductionmentioning

confidence: 99%

A Literature Review of Musculoskeletal Disorders in Handicraft Sector

Meena

Dangayach

Bhardwaj

2016

International Journal of Applied Industrial Engineering

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“…Studies measuring and analyzing hand dimensions have been conducted among people in several countries, including Bangladesh, 14,15 Czech Republic, 16 Filipin, 17 India, [18][19][20][21] Jordan, 22,23 Nigeria, 24,25 the USA, the Netherlands, and Italy. 26 In Korea, several studies [27][28][29][30] classified hand shapes based upon statistical analyses of hand dimension data retrieved from Size Korea, the National Standard Anthropometric Surveys led by the Korean Agency for Technology and Standards, Government of Korea, or hand data collected by researchers themselves, discussed below.…”

Section: Introductionmentioning

confidence: 99%

Development of a hand classification system for smart hand wearables

Hong

Kim

Choi

2023

Journal of Industrial Textiles

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Research purpose: This study aimed to lay a research foundation for smart hand wearable design by classifying the right-hand data of 4545 adults aged 20 to 69. Further, to increase the practical applicability of the hand classification system, a hand type discrimination method and regression equations for the hand dimensions of each type were presented. Methods: This study statistically analyzed eighth Size Korea data with IBM SPSS Ver.26.0. Cluster analysis was performed to classify both finger length and circumference type. Discriminant analysis was conducted, yielding discriminant functions to aid potential smart hand wearable wearers in self-diagnosing their hand types. Linear regression analysis yielded regression equations for the detailed finger dimensions for the pattern-making of smart hand wearables. Results: The finger length type was categorized into four types: the Uphill type, Downhill type, Mountain type, and Horizon type for both men’s and women’s hands. The finger circumference type was categorized into two types, the Cone and Cylinder types, for both men’s and women’s hands. The discriminant function showed a mean accuracy rate of 89.9% and the regression equations a mean explanatory power of 72.9%. Conclusion: The hand classification system proposed in this study aimed to improve the fingertip fit of smart hand wearable products by analyzing the configuration of motion tracking gloves or haptic gloves. In addition, considering the practical applicability for both wearers and designers of smart hand wearables, a discrimination method of finger types for wearers’ self-diagnosis and regression functions of finger dimensions for designers’ pattern making were provided.

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“…By using proper anthropometric data, ergonomic interventions and technological advancement, musculoskeletal disorders can be reduced, fatigue and productivity increased [3]. These tools further can also be used to increase turnover rates and employee engagement [4][5][6][7][8][9][10][11][12][13][14][15]. The present study was undertaken with the following objectives:…”

Section: Introductionmentioning

confidence: 99%