access icon free Sensing texture using an artificial finger and a data analysis based on the standard deviation

The results from experiments with a screen-printed piezoelectric sensor, mounted on an artificial finger-tip and including a cosmetic covering, are shown to detect surface information from regular texture patterns. For the automatic control of an artificial hand and to feedback information to the amputee, an algorithm has been developed based on the standard deviation (SD) of signal data from the sensor. The SD analysis for texture detection is novel as it uses a combination of arithmetic processes. It windows the data sequentially and calculates the SD of the data in the windows and then averages the SDs. The output from the algorithm is the frequency spectrum of a signal. Plots, from the output of the algorithm, show events that correspond to the cyclic waveforms produced from the regularity of object surface patterns. The results from the algorithm are confirmed with an analysis of the signals using fast Fourier transforms.

Inspec keywords: surface topography measurement; surface texture; electric sensing devices; piezoelectric transducers; fast Fourier transforms; prosthetics; data analysis

Other keywords: artificial hand; artificial finger-tip; SD analysis; data analysis; standard deviation analysis; sensing texture detection; frequency signal spectrum; automatic control; fast Fourier transform; screen-printed piezoelectric sensor; arithmetic processing; surface information detection

Subjects: Spatial variables measurement; Piezoelectric devices; Function theory, analysis; Spatial variables measurement; Sensing devices and transducers; Prosthetics and other practical applications; Prosthetics and orthotics; Sensing and detecting devices; Integral transforms

References

    1. 1)
      • 26. Kandel, E., Schwartz, J., Jessell, T.: ‘Principles of neural science’ (McGraw Hill, 2000, 4th edn.), no. 1.
    2. 2)
    3. 3)
      • 25. Vallbo, A.B., Johansson, R.S.: ‘Properties of cutaneous mechanoreceptors in the human hand related to touch sensation’, Hum. Neurobiol., 1984, 3, (1), pp. 314.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • 34. Tan, D.W., Schiefer, M.A., Keith, M.W., et al: ‘A neural interface provides long-term stable natural touch perception’, Sci. Transl. Med., 2104, 6, (257), pp. 115.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 31. Lederman, S.J., Klatzky, R.L., Hamilton, C.L., et al: ‘Perceiving roughness via a rigid probe: psychophysical effects of exploration speed and mode of touch’, Haptics-e Electron. J. Haptic. Res., 1999, 1, pp. 121.
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • 20. Cotton, D.P.J., Cranny, A., White, N.M., et al: ‘Design and development of integrated thick film sensors for prosthetic hands’. Proc. Seventh Biennial Conf. Engineering Systems Design Analysis, 2004, pp. 573589.
    18. 18)
      • 12. Grollius, S., Kuhbauch, B., Gauterin, F.: ‘Development of a three-dimensional road texture measurement as a first step towards tyre road contact simulation’. Proc. IMEchE Part D Journal of Automobile Engineering, 2012, pp. 19.
    19. 19)
    20. 20)
    21. 21)
      • 23. Jamali, N., Sammut, C.: ‘Material classification by tactile sensing using surface textures’. IEEE Int. Conf. Robot Auto, Anchorage Alaska, 2010, pp. 23362341.
    22. 22)
    23. 23)
    24. 24)
    25. 25)
      • 35. Vega-Bermudez, F., Johnson, K.O.: ‘SA1 and RA receptive fields, response variability, and population responses mapped with a probe array’, J. Neurophysiol., 1999, 81, pp. 27012710.
    26. 26)
    27. 27)
      • 14. Haopeng, L., Flierl, M.: ‘Sift-based improvement of depth imagery’. IEEE Int. Conf. Multmedia Expo ICME, 2011, pp. 16.
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
      • 21. Muridan, N., Chappell, P.H., Cotton, D.P.J., et al: ‘Detection of slip from multiple sites in an artificial finger’, Sens. Appl. XV, Heriot Watt University, Edinburgh, Scotland, 05–07 Oct 2009, IOP Publishing, pp. 16.
    33. 33)
    34. 34)
      • 26. Kandel, E., Schwartz, J., Jessell, T.: ‘Principles of neural science’ (McGraw Hill, 2000, 4th edn.), no. 1.
    35. 35)
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