Predictive Shoulder Kinematics of Rehabilitation Exercises Through Immersive Virtual Reality
Predictive Shoulder Kinematics of Rehabilitation Exercises Through Immersive Virtual Reality
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DOI：
10.1109/access.2022.3155179
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Michael Powell;Aviv Elor;Ash Robbins;Sri Kurniawan;Mircea Teodorescu
中科院分区：
文献类型：
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作者：
Michael Powell;Aviv Elor;Ash Robbins;Sri Kurniawan;Mircea Teodorescu
目的：由于全球COVID 19大流行扰乱了社区和面对面的医疗保健实践，远程医疗的采用迅速加速。虽然远程保健在增强远程治疗的可及性方面具有初步效益，但由于失去了实际操作的评价工具，身体康复受到严重限制。本文提出了一种沉浸式虚拟现实（iVR）管道，用于通过应用机器学习患者观察来复制物理治疗成功指标。研究方法：我们展示了一种训练梯度提升决策树的方法，用于运动学估计，以使用现成的iVR系统复制移动性和强度指标。在为期两个月的研究中，一组用户在iVR游戏中完成身体康复练习时收集了训练数据。利用这些数据，我们对基于iVR的运动捕捉数据和OpenSim生物力学模拟进行了训练。结果如下：我们的最终模型表明，OpenSim的上肢运动学可以使用HTC Vive头戴式显示系统准确预测，关节角度的平均绝对误差小于0.78°，关节扭矩小于2.34 Nm。此外，这些预测对于运行时间估计是可行的，在锻炼会话期间具有大约0.74 ms的预测速率。结论：这些研究结果表明，iVR与机器学习相结合可以作为一种有效的媒介，用于收集基于证据的远程医疗患者成功指标。重要性：我们的方法可以通过为治疗师提供远程评估所需的指标，帮助增加使用现成的IVR头戴式显示系统进行身体康复的可访问性。
Objective: The adoption of telehealth has rapidly accelerated owing to the global COVID19 pandemic disrupting communities and in-person healthcare practices. While telehealth had initial benefits in enhancing accessibility for remote treatment, physical rehabilitation has been heavily limited owing to the loss of hands-on evaluation tools. This paper presents an immersive virtual reality (iVR) pipeline for replicating physical therapy success metrics through applied machine learning of patient observation. Methods: We demonstrate a method of training gradient boosted decision-trees for kinematic estimation to replicate mobility and strength metrics using an off-the-shelf iVR system. During the two-month study, training data were collected while a group of users completed physical rehabilitation exercises in an iVR game. Utilizing this data, we trained on iVR-based motion capture data and OpenSim biomechanical simulations. Results: Our final model indicates that upper-extremity kinematics from OpenSim can be accurately predicted using the HTC Vive head-mounted display system with a Mean Absolute Error less than 0.78° for joint angles and less than 2.34 Nm for joint torques. Additionally, these predictions are viable for runtime estimation, with approximately a 0.74 ms rate of prediction during exercise sessions. Conclusion: These findings suggest that iVR paired with machine learning can serve as an effective medium for collecting evidence-based patient success metrics for telehealth. Significance: Our approach can help increase the accessibility of physical rehabilitation with off-the-shelf iVR head-mounted display systems by providing therapists with the metrics needed for remote evaluation.