Browsing by Author "Garikayi, Talon"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- ItemDevelopment of a robust myoelectric control architecture for lower limb robotic prostheses applications.(Stellenbosch : Stellenbosch University, 2018-12) Garikayi, Talon; Van den Heever, David Jacobus; Matope, Stephen; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.ENGLISH ABSTRACT: Traumatic events such as accidents or vascular and circulatory disorders often lead to amputation of the lower limb. To increase mobility most amputees are tted with a passive prosthetics. However, the use of a passive foot with a xed ankle has short term e ects, such as asymmetric gait, increased muscle contraction on the intact side and higher metabolic energy expenditure. The long-term e ects are osteoarthritis, osteoporosis, back pain and to a large extent musculoskeletal problems. As a result, arti cial prosthetic limbs are regarded by the amputees as exotic lifeless attachments to the body and not as a non-biological extension of the human body. Mechatronic systems coupled with intelligent control architectures provide the platform to restoring an amputee's overall mobility related lifestyle. However, the recovered gait is largely in uenced by the extent of amputation and functional level of the prosthesis. The transtibial osteomyoplastic amputation technique offers residual muscles that are active throughout the gait cycle. These muscles offer potential sites for extracting surface electromyography (sEMG) signals. The study presents a novel methodology which seeks to utilise these residual signals to control an artificial limb by predicting the human movement intentions. A protocol was developed for the acquisition and analysis of electromyography signals from the identified muscles. The available SENIAM and ISEK standards were found to be insuficient during the recording of signals from the residual stump as some of the anatomical landmarks were missing. The Soleus muscle responsible for plantarflexion was not accessible on the residual limb thereby providing challenges on using the SENIAM standards for selecting a muscle for the plantarflexion movement. Tibialis anterior, Medialis Gastrocnemius and Lateralis Gastrocnemius muscles were able to provide sEMG signals with sufficient signal properties for developing a myoelectric pattern recognition architecture. The main goal was to develop a robust intelligent control system architecture for a robotic prosthetic lower limb capable of enhancing human mobility with great stability. The functionality of a robotic limb is highly governed by kinetics, kinematics and the dynamics of the mechanical structure when interfaced with the human body. Therefore, the structure and parameters of the actuation model for complex joint angle prediction and an intuitive neural interface mechanism for intention detection were developed based on experimental results from biomechanics experiments. A pattern recognition algorithm was developed based on 23 signal features. Principal component analysis was used for dimensionality reduction on the extracted feature set. A total of 22 classifiers were tested and the Linear Support Vector Machine produced an average of 100% classification accuracy on training data with 20% of the training data being reserved for validation. The intelligent architecture produced an average of 99.25% classification accuracy on new unlabelled test data. The system was optimised using force sensitive resistors to detect heel strike, toe o and beginning of the swing and stance phases of gait. A dual inertial measurement system was used to predict the position of the limb in space thereby providing feedback on limb performance to the main controller. The use of adaptive lters on signal acquisition improved signal quality and the use of Kalman lters on feedback sensors provided a robust system which was able to achieve the desired control objective even in the event of partial or missing input signal as they predicted the intended signal based on the previously correct signal input. This study revealed that the concept developed has the potential to improve the lives of many amputees as it has the ability to restore normal gait to the satisfactory level of the amputee. The intuitive control of the prosthetic limb provided by the sEMG signals and the inertial sensor feedback system minimises the need for the situational attentiveness of the amputee with regards to the operation of the powered prosthetic.