SleeveAR: Augmented Reality for Rehabilitation using Realtime Feedback
SleeveAR: Augmented Reality for Rehabilitation using Realtime Feedback
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SleeveAR：使用实时反馈的增强现实康复
DOI：
10.1145/2856767.2856773
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Joaquim Jorge
中科院分区：
文献类型：
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作者：
Maurício Sousa;João Vieira;Daniel Medeiros;A. Arsénio;Joaquim Jorge
我们提出了一个智能用户界面，允许人们在治疗师的离线监督下自己进行康复练习。每年都有许多人受伤，需要康复。这需要大量的时间开销，因为它要求人们在治疗师的直接监督下进行特定的练习。因此，患者最好在诊所外继续进行锻炼（例如在家中，因此没有直接监督），以补充诊所内的物理治疗。然而，为了准确地完成康复任务，患者需要适当的反馈，如物理治疗师提供的，以确保这些无监督的练习正确执行。不同的方法解决这个问题，提供反馈机制，以帮助康复。不幸的是，测试对象经常报告难以完全理解所提供的反馈，这使得正确执行规定的动作变得困难。更糟糕的是，由于不正确地执行规定的运动，可能会造成受伤，这严重阻碍了康复。SleeveAR是一种提供实时、主动反馈的新方法，它使用多个投影表面来提供有效的可视化。实证评估表明，与传统的基于视频的反馈相比，我们的方法是有效的。我们的实验结果表明，我们的智能UI可以成功地指导受试者完成物理治疗师指定（并演示）的锻炼，在连续执行之间的性能提高，这是成功康复的理想目标。
We present an intelligent user interface that allows people to perform rehabilitation exercises by themselves under the offline supervision of a therapist. Every year, many people suffer injuries that require rehabilitation. This entails considerable time overheads since it requires people to perform specified exercises under the direct supervision of a therapist. Therefore it is desirable that patients continue performing exercises outside the clinic (for instance at home, thus without direct supervision), to complement in-clinic physical therapy. However, to perform rehabilitation tasks accurately, patients need appropriate feedback, as otherwise provided by a physical therapist, to ensure that these unsupervised exercises are correctly executed. Different approaches address this problem, providing feedback mechanisms to aid rehabilitation. Unfortunately, test subjects frequently report having trouble to completely understand the feedback thus provided, which makes it hard to correctly execute the prescribed movements. Worse, injuries may occur due to incorrect performance of the prescribed exercises, which severely hinders recovery. SleeveAR is a novel approach to provide real-time, active feedback, using multiple projection surfaces to provide effective visualizations. Empirical evaluation shows the effectiveness of our approach as compared to traditional video-based feedback. Our experimental results show that our intelligent UI can successfully guide subjects through an exercise prescribed (and demonstrated) by a physical therapist, with performance improvements between consecutive executions, a desirable goal to successful rehabilitation.