CPS: TTP Option: Synergy: Learning and Adaption in Pediatric Robotics

受神经系统疾病（例如脑瘫、肌肉萎缩、脊柱裂和严重头部外伤）影响的儿童通常会出现严重的残疾，包括运动控制受损。 在许多情况下，步行成为一种非功能性且令人精疲力尽的技能，需要使用辅助工具或替代功能，例如轮椅。这通常会导致这些孩子无法获得运动技能，从而失去独立性。然而，众所周知，双足运动是人类的一项基本特征，可确保获得最佳的生理运动模式。因此，对于患有神经和神经肌肉疾病的儿童来说，由于中枢神经系统的可塑性，独立行走是一个重要的康复目标，必须在特定的时间窗口内追求。换句话说，患有神经系统疾病的儿童通过辅助步行练习获得运动技能的时间很短。 这项研究工作的目的是创建并通过实验验证一套技术，这些技术构成了为患有神经系统疾病的儿童开发自适应、自平衡和模块化外骨骼机器人系统的框架。我们相信，这项研究产生的外骨骼（及其相关基础设施）将为患有神经系统疾病的儿童在生命早期的关键时期促进运动技能的获得和总体健康提供有效的工具。该研究提案开发了一种针对生理条件的数据驱动的人机建模。 这创建了预测用户行为的回归模型，而无需对复杂的人类肌肉骨骼动力学和运动控制机制进行显式建模。 此外，该研究项目将安全自适应控制问题表述为模型预测控制（MPC）问题。 在该方法中，通过解决约束有限时间最优控制问题来计算最优输入序列，其中外骨骼入侵（来自外骨骼的输入）被最小化以最大化用户促进学习的意图。 该项目进一步开发了一种稳定和防止外骨骼和儿童整体跌倒的新方法。这种方法允许佩戴外骨骼的孩子学习上述运动技能，同时减少跌倒的可能性。 该研究项目进一步评估了所开发技术的效率和功效，并为儿童创造了一种利用外骨骼来提高运动技能的新颖有趣的游戏。

Children affected by neurological conditions (e.g., Cerebral Palsy, Muscular Atrophy, Spina Bifida and Severe head trauma) often develop significant disabilities including impaired motor control. In many cases, walking becomes a non-functional and exhausting skill that demands the use of the aids or the substitution of function, such as wheelchair. This usually cause these children not to acquire locomotion skills, and consequently to lose their independence. However, it is well understood that bipedal locomotion, an essential human characteristic, ensures the best physiological motor pattern acquisition. For this reason, in children with neurological and neuromuscular diseases, independent walking is a significant rehabilitation goal that must be pursued in a specific temporal window due to the plasticity of central nervous system. In other words, children with neurological conditions have a small window of time to acquire locomotion skills through assisted walking rehearsals. The objective of this research work is to create and experimentally validate a set of technologies that form the framework for the development of adaptive, self-balancing, and modular exoskeleton robotics systems for children with neurological disorders. It is our belief that the exoskeleton (and its associated infrastructure) resulting from this research will offer an effective tool to promote locomotion skill acquisition, and in general health, during a critical period in the early life of children with neurological conditions. This research proposal develops a data-driven human-machine modeling specific to physiological conditions. This creates regression models that predict the user behavior without explicit modeling the complex human musculoskeletal dynamics and motor control mechanism. Additionally this research project formulates a safe adaptive control problem as a model predictive control (MPC) problem. In this method, an optimal input sequence is computed by solving a constrained finite-time optimal control problem where exoskeleton intrusion (input from exoskeleton) is minimized to maximize the user's intent to promote learning. This project further develops a novel approach for stabilizing and preventing fall of the exoskeleton and the child as a whole. This method allows a child wearing an exoskeleton to learn locomotion skills described above with less likelihood of falls. This research project furthermore evaluates the developed technologies in terms of efficiency and efficacy and creates a novel fun game using exoskeleton for children to promote locomotion skills.