Usefulness of Kinect sensor–based reachable workspace system for assessing upper extremity dysfunction in breast cancer patients
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Recently, the utility of the Kinect sensor–based reachable workspace analysis system for measuring upper extremity outcomes of neuromuscular and musculoskeletal diseases has been demonstrated. Here, we investigated its usefulness for assessing upper extremity dysfunction in breast cancer patients.
Twenty unilateral breast cancer patients were enrolled. Upper extremity active range of motion was captured by the Kinect sensor, and reachable workspace relative surface areas (RSAs) were obtained. The QuickDASH was completed to assess upper extremity disability. General and breast cancer–specific quality of life (QOL) were assessed by the EORTC QLQ-C30 and EORTC QLQ-BR23.
The total RSA ratio of the affected and unaffected sides ranges from 0.64 to 1.11. Total RSA was significantly reduced on the affected versus unaffected side (0.659 ± 0.105 vs. 0.762 ± 0.065; p = 0.001). Quadrant 1 and 3 RSAs were significantly reduced (0.135 ± 0.039 vs. 0.183 ± 0.040, p < 0.001; 0.172 ± 0.058 vs. 0.217 ± 0.031, p = 0.006). Total RSA of the affected side was strongly correlated with the numeric pain rating scale during movement (r = − 0.812, p < 0.001) and moderately with the QuickDASH (r = − 0.494, p = 0.027). Further, quadrant 3 RSA was correlated with EORTC QLQ-C30 role functioning (r = 0.576, p = 0.008) and EORTC QLQ-BR23 arm symptoms (r = − 0.588, p = 0.006) scales.
The Kinect sensor–based reachable workspace analysis system was effectively applied to assess upper extremity dysfunction in breast cancer patients. This system could potentially serve as a quick and simple outcome measure that provides quantitative data for breast cancer patients.
KeywordsKinect Reachable workspace Upper extremity Disability Breast cancer
Compliance with ethical standards
The study protocol was approved by the Institutional Review Board of Konkuk University Medical Center.
Conflict of interest
The authors declare that they have no conflicts of interest.
Informed consent was obtained from all individual participants included in the study.
- 1.Stewart BW, Wild CP (2014) World Cancer Report 2014, LyonGoogle Scholar
- 5.Kibar S, Dalyan Aras M, Unsal Delialioglu S (2017) The risk factors and prevalence of upper extremity impairments and an analysis of effects of lymphoedema and other impairments on the quality of life of breast cancer patients. Eur J Cancer Care 26(4). https://doi.org/10.1111/ecc.12433 CrossRefGoogle Scholar
- 8.Kootstra JJ, Dijkstra PU, Rietman H, de Vries J, Baas P, Geertzen JH, Hoekstra HJ, Hoekstra-Weebers JE (2013) A longitudinal study of shoulder and arm morbidity in breast cancer survivors 7 years after sentinel lymph node biopsy or axillary lymph node dissection. Breast Cancer Res Treat 139(1):125–134. https://doi.org/10.1007/s10549-013-2509-y CrossRefPubMedGoogle Scholar
- 14.Han JJ, Kurillo G, Abresch RT, Nicorici A, Bajcsy R (2013) Validity, reliability, and sensitivity of a 3D vision sensor-based upper extremity reachable workspace evaluation in neuromuscular diseases. PLoS Curr 5. https://doi.org/10.1371/currents.md.f63ae7dde63caa718fa0770217c5a0e6
- 22.Guan Y, Yokoi K (2006) Reachable space generation of a humanoid robot using the Monte Carlo method. 2006 IEEE/RSJ Int Conf Intell Robot Sys Beijing. 2006:1984–1989. https://doi.org/10.1109/IROS.2006.282406
- 25.EORTC. EORTC QOL Questionnaires. https://qol.eortc.org/questionnaires/. Accessed 12 Mar 2019
- 28.Gritsenko V, Dailey E, Kyle N, Taylor M, Whittacre S, Swisher AK (2015) Feasibility of using low-cost motion capture for automated screening of shoulder motion limitation after breast cancer surgery. PLoS One 10(6):e0128809. https://doi.org/10.1371/journal.pone.0128809 CrossRefPubMedPubMedCentralGoogle Scholar