CRII: SCH: A Framework for Optimizing Exoskeleton-Assisted Walking Performance in Children with Cerebral Palsy

行走能力对健康和幸福至关重要，特别是在儿童发展期间。患有脑瘫的儿童有神经肌肉损伤，通常会削弱行走能力。可穿戴外骨骼可能会为手术和标准的物理治疗提供一个令人信服的赞誉。然而，为了有效，外骨骼辅助康复必须为每个人量身定做。通过寻求提高外骨骼辅助步态训练的有效性，从这项提议中获得的知识将为未来的外骨骼干预研究提供基础，该研究将寻求改善脑瘫患者潜在的步态功能和增加习惯性体力活动水平。这项研究的结果有可能改变儿童步态障碍的治疗，导致改善健康，并减轻脑瘫患者的经济负担。除了科学和社会影响外，该项目还试图通过向不同的学生群体提供跨学科培训，扩大在科学、技术和经济研究领域中代表性不足的个人的研究参与。这项研究将通过提升生物工程创新中心和生物工程博士项目的研究形象，对北亚利桑那大学产生积极影响。机器学习技术已经开始显示出在改善人与可穿戴机器人交互方面的潜力，但迄今为止的研究主要集中在单关节和短持续时间上。这项建议的目的是利用计算智能来建立一个框架，适用于优化患者特定的协助，并在使用可穿戴外骨骼进行训练期间增强神经肌肉的参与。研究人员将在外骨骼平台上实现一个优化框架，该框架可以从外骨骼辅助的生物力学响应中学习，以快速收敛到辅助关节力矩的时间和大小的最佳控制策略。第一个目标是建立一种外骨骼控制算法的优化算法和参数，该外骨骼控制算法基于行走性能适当地适应膝关节和脚踝辅助。第二个目标是进行一项试点研究，以确定在脑瘫儿童连续的日常步态训练中，膝盖和脚踝外骨骼辅助是如何影响步态生物力学和神经肌肉控制的。预计这项研究将提高对算法参数的理解，该算法参数需要考虑步幅到步幅和会话到会话的可变性，用于步态康复的外骨骼控制的纵向优化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The ability to walk is essential for health and well-being, particularly during childhood development. Children with cerebral palsy have neuromuscular impairments that often diminish walking capacity. Wearable exoskeletons may offer a compelling compliment to surgery and standard physical therapy. However, to be effective, exoskeleton assisted rehabilitation must be finely tailored to each individual. By seeking to improve the effectiveness of exoskeleton-assisted gait training, knowledge gained from this proposal will provide the basis for future exoskeleton intervention studies that will seek to improve underlying gait function and increased levels of habitual physical activity in cerebral palsy. The outcomes of this research have the potential to transform the treatment of pediatric gait disorders, lead to improved health, and reduce the economic burden for individuals with cerebral palsy. In addition to the scientific and societal impacts, this project seeks to broaden research participation from individuals underrepresented in STEM fields by offering interdisciplinary training to a diverse cohort of students. This research will positively impact Northern Arizona University by elevating the research profile of the Center of Bioengineering Innovation and Bioengineering PhD program.Machine learning techniques have begun to demonstrate potential in improving human-wearable robot interactions but investigations to date have focused on single joints and short durations. The purpose of this proposal is to utilize computational intelligence to establish a framework suitable for optimizing patient-specific assistance and enhancing neuro-muscular participation during training with wearable exoskeletons. Investigators will implement, in an exoskeleton platform, an optimization framework that can learn from biomechanical responses to exoskeleton assistance to quickly converge on an optimal control strategy for the timing and magnitude of assistive joint torques. The first goal is to establish the optimization algorithms and parameters of an exoskeleton control algorithm that suitably adapts knee and ankle assistance based on walking performance. The second goal is to conduct a pilot study to determine how knee and ankle exoskeleton assistance, optimized for improving lower-extremity posture, affects gait biomechanics and neuromuscular control during consecutive daily gait training in children with cerebral palsy. It is anticipated that this research will lead to an improved understanding of the algorithm parameters needed to account for stride-to-stride and session-to-session variability for longitudinal optimization of exoskeleton control for gait rehabilitation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.