Sensing texture using an artificial finger and a data analysis based on the standard deviation. Issue 8 (1st November 2015)

• Record Type: Journal Article
• Authors: Chappell, Paul H.; Muridan, Norasmahan; Hanif, N. Hazrin H. Mohamad; Cranny, Andy; White, Neil M.
• Is Part Of: IET science, measurement & technology. Volume 9:Issue 8(2015)
• Page Start: 998
• Page End: 1006
• Publication Date: 2015-11-01
• Subjects: data analysis -- prosthetics -- piezoelectric transducers -- surface topography measurement -- surface texture -- fast Fourier transforms -- electric sensing devices; sensing texture detection -- data analysis -- standard deviation analysis -- screen‐printed piezoelectric sensor -- artificial finger‐tip -- surface information detection -- automatic control -- artificial hand -- SD analysis -- arithmetic processing -- frequency signal spectrum -- fast Fourier transform; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2015.0003
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : 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.