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学和神经控制所决定的人类运动性能的科学理解，创建一个促进机器人学和生物力学发展的计算机-人类框架，以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方法。目前的技术不允许实时进行详细的运动重建，这限制了它们在临床环境中的使用。这项工作将把理论与软件、硬件和传感技术相结合，利用动态的、主动控制的特定于受试者的肌肉骨骼模型来合成人体运动，并向受试者提供实时视觉反馈。该项目将提供开源算法和指标，用于量化人类表现，并了解修改这些指标的潜在运动特征。该项目开发的能力将通过实现实时人体运动合成对社会产生革命性影响，在康复、物理治疗、人-机器人互动、运动学和职业生物力学方面具有潜在应用。新的控制框架和模型经过运动捕捉实验验证，将通过在线储存库传播给研究人员。项目成果将包括：(1)用于基于任务的控制的人体肌肉骨骼系统的计算模型；(2)基于受试者的生理约束的运动表征的综合性能度量；(3)使用生物力学模型合成运动的控制和模拟算法，该生物力学模型准确地匹配实验数据，补偿测量误差，并实时可视化模型及其运动。为此，将首先创建基于任务的人体运动模型。将进行运动捕捉实验来验证模型并微调特定于对象的参数。然后，生成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，稳健控制和仿真算法将与计算系统集成，以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，以改善特定于对象的运动特征。最后，这些标准将被整合到一个反馈机制中，该反馈机制将在视觉上建议修改后的轨迹，以实现对受试者的最佳运动。

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.