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KPI Science News
ISSN 2617-5509 · e‑ISSN 2663-7472 Open Access · CC BY 4.0
Vol. · Issue 3 · 2019 Aug 13, 2019 article

OPTICAL FEEDBACK BASED ON THE PHOTOMETRYBY ELLIPSOIDAL REFLECTOR IN BIONIC FINGERS APPLICATION

KV
Kostiantyn P. Vonsevych Corresponding Igor Sikorsky Kyiv Polytechnic Institute, Ukraine Ukraine
MB
Mikhail O. Bezuglyi Igor Sikorsky Kyiv Polytechnic Institute, Ukraine Ukraine
OP
Olha A. Prytula Igor Sikorsky Kyiv Polytechnic Institute, Ukraine Ukraine
PublishedAug 13, 2019
KPISN 3 VOL · 3
VOL · ISS 3 · 2019 View issue

Abstract

Background. The investigation focus is the specificity of effective recognition of surface types during the interaction between finger prosthesis and manipulation objects, and organization of optical feedback system in the control module of bionic limb. Objective. The purpose of the paper is development and testing of method of optical feedback organization in the systems of bionic prostheses of human hand fingers. Methods. The developed system of optical feedback is based on the microcontroller measuring device with infrared (IR) optical emitter and sensor, ellipsoidal reflector, and artificial neural network. Results. During the research the comparison of application effectivity of optical feedback system with ellipsoidal reflector and without one was performed. The analysis was performed by classification of set with twelve, eleven, and ten types of surfaces of investigated specimens with the means of artificial neural network. The obtained accuracy of surface recognition for the system without an ellipsoidal reflector was 77%, 82% and 87%. In turn, the accuracy of surface recognition in the application of the ellipsoidal reflector was 94%, 98%, and 100%, correspondingly. Such results prove the possibility of further use of the photometry system by ellipsoidal reflectors for organization of constructive parts or complete feedback modules of the bionic fingers. Conclusions. The organization system of optical feedback based on optical emitter and sensor, ellipsoidal reflectors and artificial neural network for recognition of kinds of separate surfaces, with which can interact bionic prostheses fingers is proposed. The proposed system proved the certainty in recognition of limited set of investigated specimens. The efficiency of such system can be improved by using sensors array and wider data set for training.

Keywords

References

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