Scientific Context of the Project

Lower limb amputation is partial or complete removal of the limb due to disease, accident or trauma. Surface electromyograms (sEMG) of a large number of muscles and force sensors have been used to develop control algorithms for lower limb powered prostheses, but there are no commercial sEMG controlled prostheses available to date. Note that, unlike ankle disarticulation, transtibial amputation yields less intact lower leg muscle mass. Therefore, minimizing the use of sEMG muscle sources utilized will make powered prosthesis controller economic, and limiting the use of specifically the lower leg muscles will make it flexible. The aims are (1) to develop powered ankle prosthesis control algorithms using a neural networks approach that successfully predicts ankle angle and moment during level walking, inclined surface walking up and down and stair ascend and descend motions using exclusively sEMG in healthy population, (2) to test those in amputee population, (3) to develop multisensor support platform applications and (4) to implement the knowhow into designing powered prosthetic devices.

Innovative Aspects of the Project

• No powered prostheses to utilize solely sEMG sensors
• Minimization of sensor use in a patient specific manner
• Possibility of using novel wearable cable free sensor

Research Environment and Infrastructure

Institute of Biomedical Engineering (BME) laboratories, research infrastructure of Center for Life Sciences and Technologies (BU-LifeSci) and the newly established Motion Assistive-Patient Care Devices Development and Human Movement Analysis Center provide all necessary facilities and equipment. BME, Biomechanics Laboratory (https://bme.boun.edu.tr/biomechanics-laboratory) is fully equipped to study muscle mechanics and musculoskeletal mechanics. In addition, relevant laboratories of Physics and Electronical Electronics Engineering Departments as well as the Clean Room facility in BME- BU-LifeSci will support all planned R&D towards targeted neurotechnological device development.

Preferred Academic Background

Mechanical Engineering, Electrical and Electronics Engineering, Physics

Required GRE Score

GRE Quantitative 156.00