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

Journal of Imaging

Rita Moreira¹  André Magalh฾s³  Hélder P. Oliveira¹  Philip Morrow²  Kenji Suzuki² 

[1] Faculdade de Engenharia da Universidade do Porto, R. Dr. Roberto Frias, Porto 4200-465, Portugal; E-Mail:;Faculdade de Engenharia da Universidade do Porto, R. Dr. Roberto Frias, Porto 4200-465, Portugal; E-Mail;Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência - INESC TEC, R. Campus da FEUP, Dr. Roberto Frias, Porto 4200-465, Portugal; E-Mail:

关键词: quality of life;    breast neoplasms/surgery;    range of motion;    articular;    upper extremity/physiopathology;    edema/epidemiology;    postoperative complications/epidemiology;    lymph node excision/adverse effects;   

DOI  :  10.3390/jimaging1010134

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.