Unlike the more commonly analyzed ECG or PPG data for activity
classification, heart rate time series data is less detailed, often noisier and
can contain missing data points. Using the BigIdeasLab_STEP dataset, which
includes heart rate time series annotated with specific tasks performed by
individuals, we sought to determine if general classification was achievable.
Our analyses showed that the accuracy is sensitive to the choice of
window/stride size. Moreover, we found variable classification performances
between subjects due to differences in the physical structure of their hearts.
Various techniques were used to minimize this variability. First of all,
normalization proved to be a crucial step and significantly improved the
performance. Secondly, grouping subjects and performing classification inside a
group helped to improve performance and decrease inter-subject variability.
Finally, we show that including handcrafted features as input to a deep
learning (DL) network improves the classification performance further.
Together, these findings indicate that heart rate time series can be utilized
for classification tasks like predicting activity. However, normalization or
grouping techniques need to be chosen carefully to minimize the issue of
subject variability.

心率时间序列可以用于预测活动等分类任务，但需要谨慎选择归一化或分组技术以最小化主体差异问题。

通过分组受试者来改善心率时间序列分类的性能

Improving performance of heart rate time series classification by  grouping subjects

In this paper, we present a deep learning architecture which addresses the
problem of 3D semantic segmentation of unstructured point clouds. Compared to
previous work, we introduce grouping techniques which define point
neighborhoods in the initial world space and the learned feature space.
Neighborhoods are important as they allow to compute local or global point
features depending on the spatial extend of the neighborhood. Additionally, we
incorporate dedicated loss functions to further structure the learned point
feature space: the pairwise distance loss and the centroid loss. We show how to
apply these mechanisms to the task of 3D semantic segmentation of point clouds
and report state-of-the-art performance on indoor and outdoor datasets.

本文介绍了一种深度学习架构，用于解决非结构化点云的三维语义分割问题。与以往的工作相比，我们引入了点群聚合技术，定义了初始世界空间和学习特征空间中的点邻域，并结合专用损失函数，进一步构建了学习点特征空间。我们展示了如何将这些机制应用于点云的三维语义分割任务，并在室内和室外数据集上报告了最新性能。