In this study, a new method of classification of skeleton-based motion data has been introduced. In the first stage, we performed multiscale feature extraction of rotational data. It is based on the proposed linear quaternion lifting scheme, with respect to the rotations coded by unit quaternions, which computes each scale based on the spherical linear interpolation (SLERP) prediction function and preserves the average signal value on each scale. Consequently, motion descriptors are extracted as quaternion attributes on different scales. The final recognition is performed by the nearest neighbor and minimum distance classifiers, adapted to support nonscalar features. Because of dimensionality of obtained descriptors, an attribute selection with respect to the multiresolution data has been proposed. It takes into consideration a specified number of resolutions, which is similar to low-pass filtering of the frequency domain. This method is utilized to solve the gait-based human identification problem. To validate such an application, a database containing data from 30 subjects was collected at the Human Motion Laboratory of the Polish-Japanese Academy of Information Technology (PJAIT). The obtained results were found to be satisfactory. In the best case, over 96% precision with only seven misclassified gaits of 178 samples was achieved.

在这项研究中，介绍了一种基于骨骼的运动数据分类的新方法。在第一阶段，我们对旋转数据进行了多尺度特征提取。它基于提出的线性四元数提升方案，相​​对于单位四元数编码的旋转，它基于球面线性插值（SLERP）预测函数计算每个比例，并保留每个比例的平均信号值。因此，运动描述符被提取为不同比例的四元数属性。最终识别由适合支持非标量特征的最近邻和最小距离分类器执行。由于获得的描述符的维度，已经提出了关于多分辨率数据的属性选择。它考虑了指定数量的分辨率，这类似于频域的低通滤波。该方法用于解决基于步态的人类识别问题。为了验证这种应用，在波兰日本信息技术学院（PJAIT）的人体运动实验室收集了包含30个受试者的数据的数据库。发现所获得的结果是令人满意的。在最佳情况下，在178个样本中只有7个错误分类的步态实现了超过96％的精度。在波日信息技术学院（PJAIT）的人体运动实验室收集了包含30个受试者数据的数据库。发现所获得的结果是令人满意的。在最佳情况下，在178个样本中只有7个错误分类的步态实现了超过96％的精度。在波日信息技术学院（PJAIT）的人体运动实验室收集了包含30个受试者数据的数据库。发现所获得的结果是令人满意的。在最佳情况下，在178个样本中只有7个错误分类的步态实现了超过96％的精度。