虚拟现实技术量化慢性腰痛患者的运动行为

Tommi Gröhn, S. Liikkanen, T. Huttunen, Mika Mäkinen, P. Liljeberg, P. Marttinen
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引用次数: 0

摘要

慢性腰痛(CLBP)是全球常见的肌肉骨骼问题。测量疼痛感和治疗效果一直是医疗保健的一个挑战。在这里,我们研究了使用虚拟现实(VR)程序收集的运动数据如何作为CLBP患者的客观测量。开发了一种基于VR的特定数据收集方法,并将其用于CLBP患者和健康志愿者。我们证明了VR中的运动数据可以使用逻辑回归对这两组个体进行高精度的分类。最具区别性的特征是运动的持续时间和运动速度的总变化。此外,我们表明隐马尔可夫模型可以将运动数据划分为有意义的部分,这为定义更详细的特征创造了可能性,当将来有更大的数据集可用时,有可能提高准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying Movement Behavior of Chronic Low Back Pain Patients in Virtual Reality
Chronic low back pain (CLBP) is a globally common musculoskeletal problem. Measuring the sensation of pain and the effect of a treatment has always been a challenge for healthcare. Here, we study how the movement data, collected while using a virtual reality (VR) program, could be used as an objective measurement in patients with CLBP. A specific data collection method based on VR was developed and used with CLBP patients and healthy volunteers. We demonstrate that the movement data in VR can be used to classify individuals in these two groups with a high accuracy by using logistic regression. The most discriminative features are the duration of the movements and the total variation of movement velocity. Furthermore, we show that hidden Markov models can divide movement data into meaningful segments, which creates possibilities for defining even more detailed features, with potential to improve accuracy, when larger datasets become available in the future.
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CiteScore
10.30
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