Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.AK is funded by a faculty endowment by adidas AG. MA, SKH, NM, MN, RJQ, R-DR, RJT are supported by NSF CPS grant 1739800, VA Merit Reviews A2275-R and 3056, and the NIH (5T32EB004314-15, R01 NS040547-13). MS and AG are funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 803035). AJd-A, JMF-L, and JCM are supported by coordinated grants RTI2018-097290-B-C31/C32/C33 (TAILOR project) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. MR is funded by the Lo3-ML project by the Federal Ministry for Education, Science and Technology (BMBF) (Funding No. 16ES1142K). AC, SS, and MV were supported by the European Research Council (ERC) under the project NGBMI (759370), the Einstein Stiftung Berlin, the ERA-NET NEURON project HYBRIDMIND (BMBF, 01GP2121A and -B) and the BMBF project NEO (13GW0483C)

Learning Versatile Control: Evaluating Supervised Learning Algorithms in Parameterized Skills, to Apply the Algorithm on Rest to Rest Motions

Reinforcement learning can use similarities between tasks to enable faster learning. Parameterized Skills is a state of the art reinforcement learning algorithm that does this. The algorithm consists of two steps. First, reinforcement learning is used to learn the parameters of a control policy that optimize the reward for training tasks. Second, supervised learning is used to find a function that finds the parameters as a function of the task. Four supervised learning algorithms will be implemented in Parameterized Skills and it is evaluated which of these yield the best performance in terms of accuracy and energy effiency and in terms of required time. These supervised learning algorithms are support vector regression (SVR), polynomial regression, a neural network algorithm and locally weighted linear regression. The supervised learning algorithm that yields the best performance will be applied to a task with four dimensions. Currently, Parameterized Skills has only been applied to a task with a varying goal space. The size of the data set should be increased if a more complex task is learned. It is evaluated how well the data set size scales with an increased dimension. It is concluded that locally weighted linear regression scales better in terms of the required size of the data set used to construct the controller. Locally weighted linear regression is also faster than SVR, a speed up of 2\% was achieved already on the task with a varying start and goal position, so with four dimensions. The construction time of SVR increases exponentially with the size of the data set. Therefore, it is not feasible to use SVR to construct a controller for a robot arm when it should perform tasks that vary in more than four dimensions.BMDBioMechanical EngineeringMechanical, Maritime and Materials Engineerin