Locomotion is a fundamental human skill. Real-time sensing and feedback is a promising strategy for motion training to reconstruct healthy locomotion patterns lost due to aging or disease, and to prevent injuries. In this paper, we present a pilot study on locomotion training via biomechanical modeling and a wearable haptic feedback system. In addition, a novel simulation framework for motion tracking and analysis is introduced. This unified framework, implemented within the Unity environment, is used to analyze subject’s baseline and performance characteristics, and to provide real-time haptic feedback during locomotion. The framework incorporates accurate musculoskeletal models derived from OpenSim, closed-form calculations of muscle routing kinematics and kinematic Jacobian matrices, dynamic performance metrics (i.e., muscular effort), human motion reconstruction via inertial measurement unit (IMU) sensors, and real-time visualization of the motion and its dynamics. A pilot study was conducted in which 6 healthy subjects learned to alter running patterns to lower the knee flexion moment (KFM) through haptic feedback. We targeted three gait parameters (trunk lean, cadence, and foot strike) that previous studies had identified as having an influence on reducing the knee flexion moment and associated with increased risk of running injuries. All subjects were able to adopt altered running patterns requiring simultaneous changes to these kinematic parameters and reduced their KFM to 30–85% of their baseline values. The muscular effort during motion training stayed comparable to subjects’ baseline. This study shows that biomechanical modeling, together with real-time sensing and wearable haptic feedback can greatly increase the efficiency of motion training.

中文翻译：

---

通过生物力学模型和可穿戴触觉反馈系统进行运动训练的初步研究

运动是人类的一项基本技能。实时感测和反馈是一种有前途的运动训练策略，可以重建因衰老或疾病而丢失的健康运动模式，并防止受伤。在本文中，我们提出了通过生物力学建模和可穿戴触觉反馈系统进行运动训练的初步研究。此外，介绍了一种新颖的运动跟踪和分析仿真框架。该统一框架在Unity环境中实施，用于分析受试者的基线和表现特征，并在运动过程中提供实时触觉反馈。该框架整合了来自OpenSim的精确肌肉骨骼模型，肌肉路径运动学和运动雅可比矩阵的封闭式计算，动态性能指标（例如，肌肉力量），通过惯性测量单元（IMU）传感器进行人体运动重建，以及运动及其动力学的实时可视化。进行了一项初步研究，其中6名健康受试者学会了通过触觉反馈来改变跑步模式以降低膝盖屈曲力矩（KFM）。我们的目标是三个步态参数（躯干倾斜，节奏和脚底敲击），先前的研究已经确定这些参数对减少膝关节屈曲力矩有影响，并且与增加跑步受伤的风险有关。所有受试者均能够采用改变后的跑步方式，要求同时更改这些运动学参数，并将其KFM降低至基线值的30–85％。运动训练期间的肌肉力量保持与受试者的基线相当。这项研究表明，生物力学建模，