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

受神经系统疾病影响的儿童（例如，脑性麻痹、肌肉萎缩、脊柱裂和严重头部创伤）通常会发展为严重残疾，包括运动控制受损。 在许多情况下，步行成为一种非功能性和消耗性的技能，需要使用辅助工具或功能替代品，如轮椅。这通常会导致这些儿童无法获得运动技能，从而失去独立性。然而，众所周知，双足运动，一个基本的人类特征，确保最好的生理运动模式采集。正因如此，在患有神经和神经肌肉疾病的儿童中，由于中枢神经系统的可塑性，独立行走是必须在特定时间窗内追求的重要康复目标。换句话说，患有神经系统疾病的儿童有一个很小的时间窗口通过辅助行走练习获得运动技能。 这项研究工作的目标是创建和实验验证一套技术，形成自适应，自平衡和模块化外骨骼机器人系统的框架，为儿童神经系统疾病的发展。我们相信，这项研究产生的外骨骼（及其相关基础设施）将提供一种有效的工具，以促进运动技能的获得，并在神经系统疾病儿童早期生命的关键时期促进一般健康。该研究方案开发了一种特定于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，而无需对复杂的人体肌肉骨骼动力学和运动控制机制进行显式建模。 此外，本研究计画将一个安全的适应性控制问题规划为一个模型预测控制（MPC）问题。 在该方法中，通过求解受约束的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新的方法，用于稳定和防止外骨骼和儿童整体摔倒。该方法允许穿戴外骨骼的儿童学习上述运动技能，而福尔斯的可能性较小。 该研究项目进一步评估了开发的技术的效率和功效，并为儿童创造了一种使用外骨骼的新颖有趣的游戏，以促进运动技能。

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.