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Formulating grip strength and key pinch strength prediction models for Taiwanese: a comparison between stepwise regression and artificial neural networks

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Abstract

Forceful exertions of the hand have been recognized as a risk factor for the development of work-related musculoskeletal disorders (MSDs) of the upper extremity. Gripping and pinching are frequently used hand strength in various occupational activities and in clinical evaluation of the hand. Therefore, formulating grip and pinch prediction models with easily obtainable personal parameters will help facilitate the design and evaluation of workplace environments or facilitate the hand impairment or progression assessments. This study developed maximum voluntary contraction (MVC) grip and key pinch strength prediction models using regression method and artificial neural networks (ANN). Thirty-three right-handed Taiwanese (19 males and 14 females) voluntarily participated in this study. Grip regression model (adjusted R 2 = 0.897) was formulated based on gender, hand length, weight and thumb length. Key pinch regression model (adjusted R 2 = 0.845) was developed using gender, fingertip to root digit 5 (little finger length), weight and maximum hand breadth. The ANN models predict grip and key pinch strength more accurately than regression models with smaller root mean square errors (RMSEs). However, no significant differences were found between values predicted by both kinds of equations.

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References

  • Angst F, Drerup S, Werle S, Herren DB, Simmen BR, Göldhahn J (2010) Prediction of grip and key pinch strength in 978 healthy subjects. BMC Musculoskel Disord 11:94

    Article  Google Scholar 

  • Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Article  Google Scholar 

  • BLS (2013) Nonfatal occupational injuries and illnesses requiring days away from work 2012. US Bureau of Labor Statistics. http://www.bls.gov/news.release/pdf/osh2.pdf

  • Bohannon RW, Peolsson A, Massy-Westropp N, Desrosiers J, Bear-Lehman J (2006) Reference values for adult grip strength measured with a Jamar dynamometer: a descriptive meta-analysis. Physiotherapy 92:11–15

    Article  Google Scholar 

  • Caldwell LS, Chaffin DB, Dukes-Dobos FN, Kroemer KHE, Laubach LL, Snook SHL, Wasserman DE (1974) A proposed standard procedure for static muscle strength testing. Am Ind Hyg Assoc J 35:201–206

    Article  Google Scholar 

  • Chandrasekaran B, Ghosh A, Prasad C, Krishnan K, Chandrasharma B (2010) Age and anthropometric traits predict handgrip strength in healthy normals. J Hand Microsurg 2(2):58–61

    Article  Google Scholar 

  • Chong CK, Tseng CH, Wong MK, Tai TY (1994) Grip and pinch strength in Chinese adults and their relationship with antropometric factors. J Formos Med Assoc 93(7):616–621

    Google Scholar 

  • Dianat I, Haslegrave CM, Stedmon AW (2012) Methodology for evaluating gloves in relation to the effects on hand performance capabilities: a literature review. Ergonomics 55:1429–1451

    Article  Google Scholar 

  • Didomenico A, Nussbaum MA (2003) Measurement and prediction of single and multi-digit finger strength. Ergonomics 46(15):1531–1548

    Article  Google Scholar 

  • Eksioglu M, Fernandez JE, Twomey JM (1996) Predicting peak pinch strength: artificial neural networks vs. regression. Int J Ind Ergon 18:431–441

    Article  Google Scholar 

  • EUMUSC (2013) Musculoskeletal Health in Europe Report 5.0, The European Musculoskeletal Conditions Surveillance and Information Network. http://www.eumusc.net/myUploadData/files/Musculoskeletal%20Health%20in%20Europe%20Report%20v5.pdf

  • Fransson C, Winkel J (1991) Hand strength: the influence of grip span and grip type. Ergonomics 34(7):881–892

    Article  Google Scholar 

  • Günther CM, Bürger A, Rickert M, Schulz CU (2008) Key pinch in healthy adults: normative values. J Hand Surg Eur 33(2):144–148

    Article  Google Scholar 

  • Hallbeck MS, Rice VJB, Fathallah FA, Williams VH (1989) Relationship between hand anthropometry and maximal pinch force. In: Mital A (ed) advances in industrial ergonomics and safety I. Taylor and Francis, London

    Google Scholar 

  • Hanten WP, Chen WY, Austin AA, Brooks RE, Carter HC, Law CA, Morgan MK, Sanders DJ, Swan CA, Vanderslice AL (1999) Maximum grip strength in normal subjects from 20 to 64 years of age. J Hand Surg 12(3):193–200

    Google Scholar 

  • Ho RWD, Chang SY, Wang CW, Hwang MH (2000) Grip and key pinch strength: Norms for 15- to 22-year-old Chinese students. Chin Med J TPE 63(1):21–27

    Google Scholar 

  • Hornik K, Stichcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Hossain MG, Zyroul R, Pereira BP, Kamarul T (2011) Multiple regression analysis of factors influencing dominant hand grip strength in an adult Malaysian population. J Hand Surg Eur 37E(1):65–70

    Google Scholar 

  • HSE (2013) Musculoskeletal Disorders in Great Britain 2013. Health and Safety Executive, Great Britain. http://www.hse.gov.uk/statistics/causdis/musculoskeletal/msd.pdf

  • IOSH (2011) Screening and Disability Prevention for Musculoskeletal Disorders of Workers in Taiwan. Annual Report of Labor Safety and Health Research: IOSH100 -M318. Institute of Labor, Occupational Safety and Health, Taipei, Taiwan

  • Klum M, Wolf MB, Hahn P, Leclère FM, Bruckner T, Unglaub F (2012) Predicting grip strength and key pinch using anthropometric data, DASH questionnaire and wrist range of motion. Arch Orthop Traum Surg 132(12):1807–1811

    Article  Google Scholar 

  • Kovacs K, Splittstoesser R, Maronitis A, Marras WS (2002) Grip force and muscle activity differences due to glove type. Am Ind Hyg Assoc J 63:269–274

    Article  Google Scholar 

  • Lee SJ, Kong YK, Lowe BD, Song S (2009) Handle grip span for optimising finger-specific force capability as a function of hand size. Ergonomics 52(5):601–608

    Article  Google Scholar 

  • Mathiowetz V, Kashman N, Volland G, Weber K, Dowe M (1985) Grip and pinch strength: normative data for adults. Arch Phys Med Rehabil 66:69–74

    Google Scholar 

  • Napier JR (1956) The prehensile movements of the human hand. J Bone Jt Surg 38B:902–913

    Google Scholar 

  • Nazaruddin ZT (2005) Grip strength prediction for Malaysian industrial workers using artificial neural networks. Int J Ind Ergon 35:807–816

    Article  Google Scholar 

  • NIOSH (1997) Musculoskeletal disorders and workplace factors—a critical review of epidemiologic evidence for work-related musculoskeletal disorders of the neck, upper extremity, and low back. In: Bernard BP (ed) DHHS (NIOSH) publication 97-141. National Institute of Occupational Safety and Health, Cincinnati

    Google Scholar 

  • Okunribido OO (2000) A survey of hand anthropometry of female rural farm workers in Ibadan, Western Nigeria. Ergonomics 43:282–292

    Article  Google Scholar 

  • OSHA (2000) Ergonomics: the study of work. Occupational Safety and Health Administration publication 3125

  • Petrofsky JS, Williams C, Kamen G, Lind AR (1980) The effect of handgrip span on isometric exercise performance. Ergonomics 23(12):1129–1135

    Article  Google Scholar 

  • Pheasant ST (1983) Sex differences in strength—some observations on their variability. Appl Ergon 14:205–211

    Article  Google Scholar 

  • Portney LG, Watkins MP (2008) Foundations of clinical research: applications to practice, 3rd edn. Pearson & Prentice Hall, Upper Saddle River

    Google Scholar 

  • Puh U (2010) Age-related and sex-related differences in hand and pinch grip strength in adults. Int J Rehabil Res 33(1):4–11

    Article  Google Scholar 

  • Shim JH, Roh SY, Kim JS, Lee DC, Ki SH, Yang JW, Jeon MK, Lee SM (2013) Normative measurements of grip and pinch strengths of 21st century Korean population. Arch Plast Surg 40:52–56

    Article  Google Scholar 

  • Su CY, Chien TH, Cheng KF, Su CJ (1995) A study of pinch strength in normal Taiwanese adults. Kaoshiung J Med Sci 11(2):69–78

    Google Scholar 

  • Sung PC (2014) Effects of glovebox gloves on grip and key pinch strength and contact forces for simulated manual operations with three commonly used hand tools. Ergonomics. doi:10.1080/00140139.2014.933888

    Google Scholar 

  • Vaz M, Hunsberger S, Diffey B (2002) Prediction equations for handgrip strength in healthy Indian male and female subjects encompassing a wide age range. Ann Hum Biol 29(2):131–141

    Article  Google Scholar 

  • Wang MJ, Bishu RR, Rodgers SH (1987) Grip strength changes when wearing three types of gloves. In: Proceedings of Interface 87, Rochester, New York, pp 349–354

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Acknowledgments

This study was supported by research grant NSC 98–2221–E–324–010–MY2 from the National Science Council of Taiwan.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Chaoyang University of Technology, Room R301, No. 168, Jifeng E. Rd., Wufeng District, Taichung, 41349, Taiwan, R.O.C

    Peng-Cheng Sung, Chun-Chin Hsu, Cheng-Lung Lee & Yuan-Shyi Peter Chiu

  2. Department of Cosmetic Science and Application, Lan Yang Institute of Technology, Touchen Town, Yilan County, 26141, Taiwan, R.O.C

    Huey-Long Chen

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  1. Peng-Cheng Sung
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  2. Chun-Chin Hsu
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  3. Cheng-Lung Lee
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  4. Yuan-Shyi Peter Chiu
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  5. Huey-Long Chen
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Correspondence to Peng-Cheng Sung.

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Sung, PC., Hsu, CC., Lee, CL. et al. Formulating grip strength and key pinch strength prediction models for Taiwanese: a comparison between stepwise regression and artificial neural networks. J Ambient Intell Human Comput 6, 37–46 (2015). https://doi.org/10.1007/s12652-014-0245-8

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  • DOI: https://doi.org/10.1007/s12652-014-0245-8

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