Electronic powered prosthetic device for trans-radial amputees using pattern classification.

Theron, Landolf C. (2016-12)

Thesis (MEng)--Stellenbosch University, 2016.

Thesis

ENGLISH ABSTRACT: This document presents a Masters dissertation on the development of an affordable electronic prosthetic device for transradial amputees. A mechanical prosthetic hand was converted to an electronic actuated prosthetic device. EMG signals on the forearm were classified to grant amputees natural control over the prosthetic device. Three pattern classification techniques and several feature sets were validated using an existing database (NinaPro, 2014) of amputated subjects and non-amputated subjects. This verification established the classification technique and feature sets to be implemented in the rest of the project. It was established that a self-organizing map will be used with three different feature sets. A t-test suggested that there was no statistical difference between the classification rate of amputated subjects and non-amputated subjects. The prosthetic hand and all its components were designed, manufactured and assembled. A current sensor was designed and tested. The current sensor measured the current of each motor individually to relate the torque of the motor to the grasp strength of this prototype. The reaction time of the prosthetic device was tested and could reach the same position as a non-amputated hand in 2.48 seconds. The force measured at the tip of the finger was 15.56 N which compared well with commercial devices. An Android application was developed to process the EMG signals measured by a Myo Armband. The classifier was implemented on the Android application and the user interface provided the training and live classification platform. A prosthesis guided training method was used for amputated subjects. The classification technique and three feature sets were tested on both amputated and non-amputated subjects. Different window sizes were used for the EMG data and the best feature set and window size was determined. The average training classification rate using a sample size of 15 non-amputated subjects was calculated as 96.2 % with a live classification rate of 87.2 %. The average training classification rate using a sample size of two amputated subjects was calculated as 94.3 % with a live classification rate of 85.3 %. There was no statistical difference between the different feature sets, window sizes and window shift sizes. An offline muscle verification test was done to establish which sensors were dominant for each grasp. The sensors were related to the muscles they were placed on. This verification confirmed the muscles used for each grasp type and was consistent with literature. It was concluded that a mean live classification rate of 85.3 % was achievable when amputated subjects (n = 2) used this prosthetic device. This prosthetic device prototype was developed for R7 265.54. The prototype cost are promising for developing countries like South Africa. This means that this device could be funded by medical aids or the WCF.

AFRIKAANSE OPSOMMING: Hierdie dokument bied ‘n Meestersgraad verhandeling oor die ontwikkeling van ‘n bekostigbare elektroniese prostetiese hand vir transradiale geamputeerdes. ‘n Meganiese prostetiese hand was omgeskakel na ‘n elektroniese prostetiese hand. EMG seine op die voorarm was geklassifiseer om geamputeerdes ‘n natuurlike beheer oor die prostetiese hand te gee. Drie patroon klassifiseringstegnieke en verskeie EMG kenmerk stelle was getoets met behulp van ‘n bestaande databasis (NinaPro, 2014) wat geamputeerde en ongeskonde vrywilligers se EMG data bevat. Hierdie verifikasie het die klassifikasie tegniek en kenmerk stelle vasgestel. Dit was vasgestel dat ‘n self-organiserende kaart gebruik sal word met drie verskillende kenmerk stelle. ‘n T-toets het bevestig dat daar geen statistiese verskil tussen die klassifikasie koers van geamputeerde vrywilligers en ongeskonde vrywilligers was nie. Die prostetiese hand en al sy komponente was ontwerp, vervaardig en aanmekaar gesit. ‘n Stroom sensor was ontwerp en getoets. Die stroom sensor meet die stroom van elke motor afsonderlik om die wringkrag van die motor met die greep krag van hierdie prototipe te vergelyk. Die reaksietyd van hierdie prototipe was bereken as 2.48 sekondes. Die maksimum krag wat by die punt van die middel vinger gemeet was is 15.56 N wat goed vergelyk met kommersïele produkte. ‘n Android toepassing was ontwikkel om die EMG seine te verwerk wat deur ‘n Myo Armband opgetel was. Die klassifiseerder was geïmplementeer op die Android toepassing en die gebruikerskoppelvlak het die opleiding en aanlyn klassifikasie platform gebied. ‘n Prostetiese geleide opleiding metode was gebruik vir geamputeerde vrywilligers. Die klassifikasie tegniek en drie EMG kenmerk stelle was getoets op beide geamputeerde vrywilligers en ongeskonde vrywilligers. Verskillende venster groottes is gebruik vir die EMG data en die beste kenmerk stel en venster grootte was vasgestel. Die gemiddelde opleiding klassifikasie koers onder 15 ongeskonde vrywilligers was bereken as 96,2 % met ‘n aanlyn klassifikasie koers van 87,2 %. Die gemiddelde opleiding klassifikasie koers onder twee geamputeerde vrywilligers was bereken as 94.3 % met ‘n aanlyn klassifikasie koers van 85.3 %. Dit was vasgestel dat daar geen statistiese verskil tussen die verskillende EMG kenmerk stelle en venster groottes was nie. ‘n Aflyn spier verifikasie toets was gedoen om vas te stel watter sensore dominant was vir elke greep. Die sensore hou verband met die spiere waarop hulle geplaas was. Hierdie verifikasie bevestig die spiere wat gebruik word vir elke tipe greep en was in ooreenstemming met literatuur. Die gevolgtrekking was gemaak dat ‘n aanlyn klassifikasie koers van 85.3 % bereik kan word op geamputeerde vrywilligers (n = 2). Die prostetiese hand was ontwikkel vir R7 265,54. Dit het beteken dat hierdie toestel befonds kan word deur mediese fondse of die WVF.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/100107
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