Study of the Grasp Conditions Effects on Pinch-Insertion Force Using Wavelet Transform
In this paper, the effects of different grasp types, grasp widths, coupling types, and wearing gloves on pinch-insertion force have been studied using the discrete wavelet transform method, which was proposed for extracting features from the force-time curves during the experiment that simulates the snap-fit assembly procedure. This paper uses collected Salmanzadeh and Landau  experiments data as an application for proposed methodology. This method allows to use of all information obtained from the experiment and considers the whole force exertion process instead of one point of curves and at the same time reduce the number of variables without losing information. The results obtained by applying multivariate analysis of variance (MANOVA) on the wavelet coefficients are more sensible and more precise than the results of the conventional method based on only one point of the curve. The results show that both wearing glove and grasp type significantly affect the pinch-insertion forces. The second series of results show that the effects of both grasp width and grasp type on pinch force are significant. The third result series show that both pinch-insertion forces are significantly affected by coupling type with/without wearing gloves. Further analysis was performed on the average wavelet coefficients curves that can explain the cause of MANOVA test results. The results of this paper can be used for ergonomic designing of snap-fits to reduce the potential damage of the assembly process.
2. Landau, K., U. Landau, and H. Salmanzadeh, Productivity improvement with snap-fit systems. Industrial Engineering and Ergonomics, 2009: p. 595-608.
3. Potvin, J.R., et al., Maximal acceptable forces for manual insertions using a pulp pinch, oblique grasp and finger press. International Journal of Industrial Ergonomics, 2006. 36(9): p. 779-787.
4. Salmanzadeh, H. and K. Landau, The effects of grasp conditions on maximal acceptable combined forces (pushing and pinch forces) for manual insertion of snap fasteners. Journal of Optimization in Industrial Engineering, 2014. 7(15): p. 27-35.
5. Byström, S. and C. Fransson-Hall, Acceptability of intermittent handgrip contractions based on physiological response. Human Factors, 1994. 36(1): p. 158-171.
6. Ng, P.K. and A. Saptari, A review of shape and size considerations in pinch grips. Theoretical Issues in Ergonomics Science, 2014. 15(3): p. 305-317.
7. Rohmert, W., Problems of determination of rest allowances Part 2: Determining rest allowances in different human tasks. Applied Ergonomics, 1973. 4(3): p. 158-162.
8. Shivers, C.L., G.A. Mirka, and D.B. Kaber, Effect of grip span on lateral pinch grip strength. Human factors, 2002. 44(4): p. 569-577.
9. Sonne, M.W. and J.R. Potvin, Fatigue accumulation and twitch potentiation during complex MVC-relative profiles. Journal of Electromyography and Kinesiology, 2015. 25(4): p. 658-666.
10. Mathiowetz, V., et al., Grip and pinch strength: normative data for adults. Arch Phys Med Rehabil, 1985. 66(2): p. 69-74.
11. Peebles, L. and B. Norris, Filling ‘gaps’ in strength data for design. Applied Ergonomics, 2003. 34(1): p. 73-88.
12. Lee, K.-S. and M.-C. Jung, Common patterns of voluntary grasp types according to object shape, size, and direction. International Journal of Industrial Ergonomics, 2014. 44(5): p. 761-768.
13. Chow, A.Y. and C.R. Dickerson, Determinants and magnitudes of manual force strengths and joint moments during two-handed standing maximal horizontal pushing and pulling. Ergonomics, 2016. 59(4): p. 534-544.
14. Imrhan, S.N., The influence of wrist position on different types of pinch strength. Applied Ergonomics, 1991. 22(6): p. 379-384.
15. Imrhan, S.N. and R. Rahman, The effects of pinch width on pinch strengths of adult males using realistic pinch-handle coupling. International Journal of Industrial Ergonomics, 1995. 16(2): p. 123-134.
16. Kattel, B.P., et al., The effect of upper-extremity posture on maximum grip strength. International Journal of Industrial Ergonomics, 1996. 18(5-6): p. 423-429.
17. Lee, K.-S. and M.-C. Jung, Investigation of hand postures in manufacturing industries according to hand and object properties. International Journal of Industrial Ergonomics, 2015. 46: p. 98-104.
18. Potvin, J.R., Predicting maximum acceptable efforts for repetitive tasks an equation based on duty cycle. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2012. 54(2): p. 175-188.
19. Snook, S.H., et al., Psychophysical studies of repetitive wrist flexion and extension. Ergonomics, 1995. 38(7): p. 1488-1507.
20. Malker, B., Official statistics of Sweden: Occupational disease and occupational accidents 1989. Stockholm, Sweden: National Board of Occupational Safety and Health, 1991.
21. Seo, N.J., Dependence of safety margins in grip force on isometric push force levels in lateral pinch. Ergonomics, 2009. 52(7): p. 840-847.
22. Liu, C.-C., et al., Heuristic wavelet shrinkage for denoising. Applied Soft Computing, 2011. 11(1): p. 256-264.
23. Qu, Y., et al., Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data. Biometrics, 2003. 59(1): p. 143-151.
24. Demirel, H. and G. Anbarjafari, Discrete wavelet transform-based satellite image resolution enhancement. IEEE transactions on geoscience and remote sensing, 2011. 49(6): p. 1997-2004.
25. Lai, C.-C. and C.-C. Tsai, Digital image watermarking using discrete wavelet transform and singular value decomposition. IEEE Transactions on instrumentation and measurement, 2010. 59(11): p. 3060-3063.
26. Mallat, S., A wavelet tour of signal processing: the sparse way. 2008: Academic press.
27. Al-Mulla, M.R. and F. Sepulveda, Super wavelet for sEMG signal extraction during dynamic fatiguing contractions. Journal of medical systems, 2015. 39(1): p. 167.
28. Cvetkovic, D., E.D. Übeyli, and I. Cosic, Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study. Digital signal processing, 2008. 18(5): p. 861-874.
29. González-Izal, M., et al., sEMG wavelet-based indices predicts muscle power loss during dynamic contractions. Journal of Electromyography and Kinesiology, 2010. 20(6): p. 1097-1106.
30. González-Izal, M., et al., New wavelet indices to assess muscle fatigue during dynamic contractions. World Academy of Science Engineering and Technology, 2009. 55: p. 480.
31. Phinyomark, A., C. Limsakul, and P. Phukpattaranont, Application of wavelet analysis in EMG feature extraction for pattern classification. Measurement Science Review, 2011. 11(2): p. 45-52.
32. Kroemer, K.H., Ergonomic Design for Material Handling Systems. 1997: CRC Press.
33. Yang, R. and M. Ren, Wavelet denoising using principal component analysis. Expert systems with Applications, 2011. 38(1): p. 1073-1076.
|Issue||Vol 12 No 4 (2020)|
|Grasp condition Wavelet transform Pinch force Insertion force Snap-fit|
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