A new technique could reduce the time and discomfort of adjusting to a new prosthetic knee. A collaboration between researchers from North Carolina State University, the University of North Carolina and Arizona State University has resulted in a new technique that enables more rapid "tuning" of powered prosthetic knees, allowing patients to comfortably walk with a new prosthetic device in minutes, rather than hours after the device is first fitted After receiving the prosthetic knee, the device is tuned to tweak 12 different control parameters to accommodate the specific patient and address prosthesis dynamics like joint stiffness throughout the entire gait cycle. Traditionally, a practitioner works directly with the user to modify a handful of parameters in a process that could take several hours. However, by using a computer program that utilizes reinforcement learning--a type of machine learning--to modify all 12 parameters simultaneously, the new system allows patients to use their powered prosthetic knee to walk on a level surface after approximately 10 minutes of use. "We begin by giving a patient a powered prosthetic knee with a randomly selected set of parameters," Helen Huang, co-author of a paper on the work and a professor in the Joint Department of Biomedical Engineering at NC State and UNC, said in a statement.

James W. Howse Chaouki T. Abdallah Gregory L. Heileman Department of Electrical and Computer Engineering University of New Mexico Albuquerque, NM 87131 Abstract The process of machine learning can be considered in two stages: model selection and parameter estimation. In this paper a technique is presented for constructing dynamical systems with desired qualitative properties. The approach is based on the fact that an n-dimensional nonlinear dynamical system can be decomposed into one gradient and (n - 1) Hamiltonian systems. Thus, the model selection stage consists of choosing the gradient and Hamiltonian portions appropriately so that a certain behavior is obtainable. To estimate the parameters, a stably convergent learning rule is presented.

James W. Howse Chaouki T. Abdallah Gregory L. Heileman Department of Electrical and Computer Engineering University of New Mexico Albuquerque, NM 87131 Abstract The process of machine learning can be considered in two stages: model selection and parameter estimation. In this paper a technique is presented for constructing dynamical systems with desired qualitative properties. The approach is based on the fact that an n-dimensional nonlinear dynamical system can be decomposed into one gradient and (n - 1) Hamiltonian systems. Thus, the model selection stage consists of choosing the gradient and Hamiltonian portions appropriately so that a certain behavior is obtainable. To estimate the parameters, a stably convergent learning rule is presented.

James W. Howse Chaouki T. Abdallah Gregory L. Heileman Department of Electrical and Computer Engineering University of New Mexico Albuquerque, NM 87131 Abstract The process of machine learning can be considered in two stages: model selection and parameter estimation. In this paper a technique is presented for constructing dynamical systems with desired qualitative properties. The approach is based on the fact that an n-dimensional nonlinear dynamical system can be decomposed into one gradient and (n - 1) Hamiltonian systems. Thus,the model selection stage consists of choosing the gradient and Hamiltonian portions appropriately so that a certain behavior is obtainable. To estimate the parameters, a stably convergent learning rule is presented.