Department of Systems Engineering and Automation, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain.
Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Rey Juan Carlos University, Avda. de atenas s/n, 28922 Alcorcón, Spain.
Sensors (Basel). 2018 Aug 31;18(9):2876. doi: 10.3390/s18092876.
Objective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson's disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes' displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user's performance.
客观的运动功能评估是评估康复过程有效性的重要组成部分。这些评估由临床医生使用传统的测试和量表进行。箱式和积木测试（BBT）就是这样一种量表，专注于手灵巧度评估。得分是一个人在时间窗口内能移动的方块数量。在之前的一篇论文中，提出了一种使用 Microsoft Kinect 传感器的自动化 BBT，称为自动化箱式和积木测试（ABBT）。在本文中，讨论了 ABBT 作为手灵巧度评估自动化工具的可行性。开发了一种基于 CIELab 颜色空间图像分割和最近邻（NN）规则的算法，以提高自动计数方块的可靠性。进行了一项初步研究，以评估帕金森病（PD）患者的手部运动功能。进行了三项功能评估。通过比较手动（BBT）和自动（ABBT）方法，研究了自动计数方块的成功率。分析了 ABBT 提供的附加信息，以讨论其临床意义。结果表明，手动（BBT）和自动（ABBT）评分之间具有高度相关性。ABBT 计数方块的平均成功率最低为 92%。此外，ABBT 从方块的位移中获取了额外的信息，例如平均速度和检测到方块的时间点。分析这些信息可以与健康状况（协调性和灵巧性）的指标相关联。结果表明，ABBT 是自动化单侧总体手灵巧度评估的有用工具，并提供了有关用户表现的附加信息。
