A Biomimetic Approach to Controlling Restorative Robotics

Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control. Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands. Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques. Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury. Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control.Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands.Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques.Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury

Systematic review of outcome measures of walking training using electromechanical and robotic devices in patients with stroke

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Development of a Methodology for the Quantification of Physiological Load for Soccer Players

The overall aim of this research project was to devise valid mathematical models for quantifying the physiological load (PL) of practices and competitions for female and male NCAA Division I collegiate soccer players. Data from sub-maximal and maximal effort tests were used to construct these models. After development of the physiological load quantification (PLQ) models, the validity of them occurred by comparing them to the physiological gold standard of performed work, volume of oxygen consumed (V*O2). Last, comparisons of the scores from the PLQ models to the PL scores from the previous models occurred. In combination these three studies have produced models which are physiologically realistic, have a very strong relationship with the gold standard of work performed and are unique when compared to the models previously presented in the research literature for the assessment of PL

VALIDATION OF THE BODYMEDIA MINI ARMBAND TO ESTIMATE ENERGY EXPENDITURE OF FEMALE BASKETBALL PLAYERS DURING VARIABLE INTENSITY GAME-LIKE CONDITIONS

Monitoring an athlete’s energy intake and energy expenditure (EE) is an important consideration of nutritional planning for sport conditioning and peak performance. In order to provide appropriate recommendations regarding nutritional requirements and caloric needs, an accurate determination of energy requirements is necessary. By knowing an individual’s EE, a coach or trainer may be effectively able to determine training loads and volumes necessary for periodization, and seasonal planning for a particular sport. Purpose: To examine the accuracy of the BodyMedia mini armband, to assess EE in female basketball players during various-intensity game-like conditions. Methods: A cross-sectional correlation design with multiple observations was employed. This investigation required three testing sessions, an orientation session, and 2 experimental trial sections. Trials included a maximal multistage 20-m shuttle run (Trial I) and 30-minute basketball skills session (Trial II). The independent variable for this investigation was EE estimated by the Mini armband. The dependent variable was EE determined by the indirect calorimetry (IC) method. Results: EE assessed with the Mini and EE measured with the IC method was significantly correlated for both Trial I (r= 0.839) and Trial II (r= 0.833). EE calculated by the Mini was significantly underestimated in both Trial I (9.41 ± 26.1 total kcals) and Trial II (56.71 ± 14.1 total kcals). During Trial I the underestimation of EE increased with an increase in test level and intensity. Conclusion: Due to the underestimation of EE by the Mini, the development of exercise specific algorithms to improve the estimation of EE during intermittent exercise in basketball players is warranted