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A Kinect-Based System for Upper-Body Function Assessment in Breast Cancer Patients

机译:基于Kinect的乳腺癌患者上身功能评估系统

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Common breast cancer treatment techniques, such as radiation therapy or the surgical removal of the axillary lymphatic nodes, result in several impairments in women’s upper-body function. These impairments include restricted shoulder mobility and arm swelling. As a consequence, several daily life activities are affected, which contribute to a decreased quality of life (QOL). Therefore, it is of extreme importance to assess the functional restrictions caused by cancer treatment, in order to evaluate the quality of procedures and to avoid further complications. Although the research in this field is still very limited and the methods currently available suffer from a lack of objectivity, this highlights the relevance of the pioneer work presented in this paper, which aims to develop an effective method for the evaluation of the upper-body function, suitable for breast cancer patients. For this purpose, the use of both depth and skeleton data, provided by the Microsoft Kinect, is investigated to extract features of the upper-limbs motion. Supervised classification algorithms are used to construct a predictive model of classification, and very promising results are obtained, with high classification accuracy.
机译:常见的乳腺癌治疗技术,例如放射疗法或腋窝淋巴结的手术切除,会导致女性上半身功能受损。这些障碍包括肩膀活动受限和手臂肿胀。结果,一些日常生活活动受到影响,从而导致生活质量(QOL)下降。因此,评估癌症治疗引起的功能限制,以评估手术质量并避免进一步的并发症极为重要。尽管该领域的研究仍然非常有限,并且目前可用的方法缺乏客观性,但这突出了本文提出的先驱工作的相关性,该工作旨在开发一种评估上身的有效方法功能,适合乳腺癌患者。为此,研究了Microsoft Kinect提供的深度和骨架数据的使用,以提取上肢运动的特征。监督分类算法被用于构建分类的预测模型,并获得了非常有希望的结果,具有很高的分类精度。

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