融合多分类器输出，用状态空间模型提升可穿戴设备动作识别准确率
Classification Filtering
- 构建状态空间模型，动态融合多个分类器的输出结果
- 在真实IMU数据上，相比单分类器显著提升动作识别准确率
- 专为实时计算设计，适合可穿戴设备等资源受限场景
我们研究一种流式信号，其中每个样本关联一个隐含类别。假设有多个可用分类器，各自提供不同精度的类别概率。这些分类器按照简单固定的策略使用。在此设定下，我们考虑如何融合分类器输出，并结合时间特性以提升分类准确性。为此，我们提出一种状态空间模型，并开发了适用于实时执行的滤波器。我们在基于可穿戴设备惯性测量单元（IMU）数据的动作分类应用中验证了所提滤波器的有效性。
原文摘要 · Abstract (English)
We consider a streaming signal in which each sample is linked to a latent class. We assume that multiple classifiers are available, each providing class probabilities with varying degrees of accuracy. These classifiers are employed following a straightforward and fixed policy. In this setting, we consider the problem of fusing the output of the classifiers while incorporating the temporal aspect to improve classification accuracy. We propose a state-space model and develop a filter tailored for realtime execution. We demonstrate the effectiveness of the proposed filter in an activity classification application based on inertial measurement unit (IMU) data from a wearable device.
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