CRII: CHS: Constraint Consistent, Task-Based Musculoskeletal Control Framework for Human Motion Synthesis and Immediate Feedback

这项研究的目标是通过加深我们对肌肉骨骼物理和神经控制所决定的人类运动表现的科学理解，来帮助临床医生量化受试者的运动特征并设计有效的运动训练治疗，从而创建一个推进机器人和生物力学的网络-人类框架。目前的技术不允许实时详细的运动重建，这限制了它们在临床环境中的应用。这项工作将理论与软件、硬件和传感技术相结合，通过动态的、主动控制的受试者特定肌肉骨骼模型来合成人体运动，并为人体受试者提供实时视觉反馈。该项目将提供开源算法和指标，用于量化人类的表现，并了解修改这些指标的潜在运动特征。通过实现实时人体运动合成，该项目开发的能力将对社会产生变革性影响，在康复、物理治疗、人机交互、运动机能学和职业生物力学方面具有潜在的应用。通过动作捕捉实验验证的新的控制框架和模型将通过在线存储库传播给研究人员。将研究与教育活动相结合，将为参与其中的本科生和代表性不足的学生提供新的见解和工具，以便在少数民族服务机构中开展未来的工程研究。项目成果将包括：(1)基于任务控制的人体肌肉骨骼系统的计算模型；(2)基于受试者生理约束的运动表征综合性能指标；(3)控制和仿真算法，利用生物力学模型来合成运动，精确匹配实验数据，补偿测量误差，并实时可视化模型及其运动。为此，首先将创建基于任务的人体运动模型。将进行动作捕捉实验来验证模型并微调受试者特定参数。由此产生的计算平台将用于确定长期性能统计数据和指标，以有效地表征人体运动。在第二阶段，鲁棒控制和仿真算法将与计算系统集成，使用生物力学模型合成运动。该框架将用于确定可行的修改，以改善主体特定的运动特性。最后，这些标准将被整合到一个反馈机制中，该机制将在视觉上建议对主体进行最佳运动的修改轨迹。

The goal of this research is to create a cyber-human framework that advances both robotics and biomechanics, by deepening our scientific understanding of human motor performance dictated by musculoskeletal physics and neural control, in order to assist clinicians in quantifying the characteristics of a subject's motion and designing effective motion training treatments. Current technologies do not permit detailed motion reconstruction in real time, which limits their use in clinical settings. This work will combine theory with software, hardware and sensing technology to synthesize human motion with dynamic, actively controlled subject-specific musculo-skeletal models and to provide real-time visual feedback to a human subject. The project will deliver open-source algorithms and metrics for quantifying human performance and for understanding the underlying motion characteristics that modify these metrics. The capabilities developed in this project will have a transformative impact on society by enabling real-time human motion synthesis, with potential applications in rehabilitation, physical therapy, human-robot interaction, kinesiology and occupational biomechanics. The new control framework and models, validated by motion capture experiments, will be disseminated to researchers through an online repository. Integration of the research with educational activities will equip involved undergraduates and underrepresented students with new insights and tools for developing future engineering research in a minority serving institution.Project outcomes will include: (1) computational models of the human musculoskeletal system for task-based control; (2) integrated performance metrics for motion characterization based on a subject's physiological constraints; and (3) control and simulation algorithms to synthesize movement using biomechanical models that accurately match experimental data, compensate for measurement errors, and visualize the model and its motion in real time. To these ends, task-based models of human motion will first be created. Motion capture experiments will be conducted to validate the model and to fine tune subject-specific parameters. The resulting computational platform will then be used to determine long-term performance statistics and metrics to efficiently characterize human motion. In the second phase, robust control and simulation algorithms will be integrated with the computational system to synthesize movement using biomechanical models. The framework will be used to identify feasible modifications to improve subject-specific motion characteristics. Finally, these criteria will be integrated into a feedback mechanism that will visually suggest modified trajectories for optimal motion to the subject.