Upper Limb Spasticity and Pain Quantification Using Motion Capture, EMG, EEG (Muscle Resistance) and Facial Expression
Mentor 1
Mohammad H Rahman
Location
Union Wisconsin Room
Start Date
27-4-2018 1:00 PM
Description
Many mechanisms of injury hinder the functions of the shoulder, elbow and wrist. The first phase of this experiment is to collect and analyze the biomechanical (position, velocity and acceleration) and physiological (EMG activity) measurements associated with a variety of upper extremity movements. Biomechanical signals and physiological signals will be obtained via EMG and motion capture systems. The output of these experiments will be translated into useful dynamic information, such as the torque of a movement and the relative spasticity at each given moment. The normative data will be collected from healthy subjects and compared with data collected from subject that have experienced a stroke and have limited upper extremity function. This data will ultimately lay a foundation on which new diagnostic and rehabilitation methods can be effectively developed. The next phase of this experiment will be integrating the data base with EEG signals and Facial expressions information obtained from subjects completing the same movements. The data will then be integrated into a format that will enable the user to quantifiably explore the spasticity, range of motion and pain endured during a wide variety of upper extremity movements. This tool will allow engineers, physicians and occupational therapists to more fully understand the scope of the injury and coordinate cohesively to address it.
Upper Limb Spasticity and Pain Quantification Using Motion Capture, EMG, EEG (Muscle Resistance) and Facial Expression
Union Wisconsin Room
Many mechanisms of injury hinder the functions of the shoulder, elbow and wrist. The first phase of this experiment is to collect and analyze the biomechanical (position, velocity and acceleration) and physiological (EMG activity) measurements associated with a variety of upper extremity movements. Biomechanical signals and physiological signals will be obtained via EMG and motion capture systems. The output of these experiments will be translated into useful dynamic information, such as the torque of a movement and the relative spasticity at each given moment. The normative data will be collected from healthy subjects and compared with data collected from subject that have experienced a stroke and have limited upper extremity function. This data will ultimately lay a foundation on which new diagnostic and rehabilitation methods can be effectively developed. The next phase of this experiment will be integrating the data base with EEG signals and Facial expressions information obtained from subjects completing the same movements. The data will then be integrated into a format that will enable the user to quantifiably explore the spasticity, range of motion and pain endured during a wide variety of upper extremity movements. This tool will allow engineers, physicians and occupational therapists to more fully understand the scope of the injury and coordinate cohesively to address it.
