NOVEL PERSPECTIVES ON PROSTHETIC DEVICES IN NEUROREHABILITATION
27.06.2019 kl. 09.00 - 11.00
KL. 9.00-9.30 - NEUROMUSCULOSKELETAL LIMB PROSTHESES AND NEUROREHABILITATION OF PHANTOM LIMB PAIN
Associate Professor Max Ortiz Catala, Chalmers University of Technology, Sweden
This lecture will be about novel but clinically viable technologies to restore patients’ quality of life. Using bone-anchored prosthesis (osseointegration), neuromuscular interfaces, and machine learning, Dr. Ortiz Catalan’s work resulted in the first bionic arm integrated directly into the patient’s bone, nerves, and muscles. In addition to direct skeletal attachment, this technology provides the unique opportunity to chronically record and stimulate the neuromuscular system in freely behaving humans, thus allowing to investigate complex limb motions and somatosensory perception. Dr. Ortiz Catalan will also discuss how motor decoding technology in combination with Augmented Reality can be used to treat Phantom Limb Pain, along with a novel theoretical framework for the condition and its treatment.
KL. 9.30-10.00 - PATTERN RECOGNITION MYOELECTRIC CONTROL FOR UPPER-LIMB PROSTHETIC LIMBS
Associate Professor Levi J Hargrove, Northwestern University, IL, USA
Use of pattern recognition for controlling upper-limb prosthetic limbs has been investigated for decades, with most studies being performed within controlled laboratory environments. Over the past 5 years, we have focused on investigating control performance outside of the laboratory environments for transhumeral amputees with targeted muscle reinnervation and transradial amputees with and without targeted muscle reinnervation. These studies indicate that these types of control systems perform equivalently or better than traditional control strategies and that many obstacles that were traditionally viewed as barriers to pattern recognition myoelectric control do not significantly limit performance and cause rejection of the device.
KL 10.30-11.00 - ON HANDWRITING PROCESS: MODELING AND FAULT DETECTION
Associate Professor Inés CHIHI, National Engineering School of Bizerta, Tunisia
While most research activities have focused on improving simple movements, like grasping and reaching, handwriting, which is an important means of communication, has received less attention from the assistive device point of view. Writing has inspired many researchers to propose models characterizing this biological process.
Indeed, Handwriting is considered as a mean of communication unavoidable for academic, professional and social integration. It contains a lot of information that can characterize a person and express his/ her social, academic level, intellectual component, and even the psycho-physical personality of the writer, the temperamental tendencies and psychic state (angry, relaxed, etc.).Writing is also expressed through motions of the upper limb and with the availability of advanced EMG recording systems, we hypothesize that EMG driven models can allow reconstruction of an individual handwriting.
However, EMG signals are non-stationary, stochastic in nature and very sensitive to many disturbances in EMG recordings. The characteristics of the muscles activities are easily affected by many factors, such as muscle layers, fat and tissue, abrupt changing of the electrodes positions, changes in the electrophysiology due to sweat or changes of the impedance of the electrode. All these conditions may lead to inaccurate identification of user intent challenging the reliability of the control system.
In this context, we have already proposed mathematical models characterizing some cursives Arabic letters and geometric shapes generated in various directions and a fault detection approach to overcome the noises and disturbances that can affect EMG signals during the handwriting process.
Registration: No registration is required.
All interested are welcome!
Winnie Jensen, Neural Engineering and Neurophysiology Research Interest Group, SMI, Department of Health Science and Technology, Aalborg University
Niels Jernes Vej 14, Room 4-107