Robotic Modeling, Simulation and Analysis

The models varieties are important for health and medical robotics applications. We can divide it into two main categories relevant health and medical robotics: people modeling (from tissue biomechanical to human physical and cognitive behavior) and engineered systems modeling (including information/integration flow, and open platforms and architectures). The models can be of physiology, biomechanics, environment, dynamics, geometry, state, interactions, tasks, cognition, and behavior. The models can be used for many tasks, including optimal design, planning, control, task execution, testing and validation, diagnosis and prognosis, training, and social and cognitive interaction.

Now we provide some specific examples of models needed for health care and medicine. In tele-operated or remote surgery with time delays, model of patient are required to allow natural interaction between the surgeon and the remote operating environment. Tissue models in general are needed for planning procedures, training simulators, and automated guidance systems. These are just beginning to be applied in needle based operations, but more sophisticated models would enable planning and context appropriate guidance for a wider variety of procedures, such as surgery cellular and laparoscopic surgery. Models that are realistic sufficiently to be rendered in real time would enable high fidelity surgical simulations for general training and patient specific practice conducted by surgeons. We need models of human cognition and behavior in order to provide appropriate motivational assistance for assistive healthcare robots. Physical models of a patient’s whole body are also needed for a robot to provide physical assistance for tasks such as getting out of bed or eating.

Another sample, consider a rehabilitation system that uses robotic technology for early and accurate diagnosis. Such a system would need models of the patient and his deficit in order to design appropriate treatments, and assess accurately outcomes. Ideally, the model of the patient would change after treatment. Such models are also needed for robotic technology to participate in and augment diagnosis.

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