Ekemeyong Awong Lisiane Esther, Zielinska Teresa
Faculty of Power and Aeronautical Engineering, Division of Theory of Machines and Robots, Warsaw University of Technology, 00-665 Warszawa, Poland.
Sensors (Basel). 2023 Sep 15;23(18):7925. doi: 10.3390/s23187925.
The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores-such as the silhouette coefficient, Dunn index, Calinski-Harabasz index, Davies-Bouldin index, and DS-for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.
本文的目的是开发一种方法，用于选择合适数量的聚类，以便使用具有无监督自组织映射的神经网络对人体姿势进行分组和识别。尽管无监督聚类算法已被证明在识别人体姿势方面有效，但许多工作仅限于测试哪些数据被正确或错误识别。它们常常忽视使用聚类质量评估来选择合适的组数（聚类数对应于输出神经元的数量，即姿势的数量）这一任务。使用质量分数来确定聚类数使专家无需对姿势数量做出主观决策，从而能够使用无监督学习。由于数据的高维度和变异性，专家决策（称为数据标记）可能既困难又耗时。在我们的案例中，不存在手动标记步骤。我们引入了一种新的聚类质量分数：判别分数（DS）。我们描述了使用RGB-D相机捕获的人类活动记录来选择最合适姿势数量的过程。本文还对流行的聚类质量分数（如轮廓系数、邓恩指数、卡林斯基-哈拉巴斯指数、戴维斯-布尔丁指数和DS）在姿势分类任务中的有用性进行了比较研究，并给出了DS产生结果的图形说明。研究结果表明，DS在姿势识别中具有良好的质量，能够有效地跟踪姿势的转换和相似性。
