With the popularity of mobile sensor technology, smart wearable devices open a unprecedented opportunity to solve the challenging human activity recognition (HAR) problem by learning expressive representations from the multi-dimensional daily sensor signals. This inspires us to develop a new algorithm applicable to both camera-based and wearable sensor-based HAR systems. Although competitive classification accuracy has been reported, existing methods often face the challenge of distinguishing visually similar activities composed of activity patterns in different temporal orders. In this paper, we propose a novel probabilistic algorithm to compactly encode temporal orders of activity patterns for HAR. Specifically, the algorithm learns an optimal set of latent patterns such that their temporal structures really matter in recognizing different human activities. Then, a novel probabilistic First-Take-All (pFTA) approach is introduced to generate compact features from the orders of these latent patterns to encode the entire sequence, and the temporal structural similarity between different sequences can be efficiently measured by the Hamming distance between compact features. Experiments on three public HAR datasets show the proposed pFTA approach can achieve competitive performance in terms of accuracy as well as efficiency.

中文翻译：

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通过概率先通学习人类活动识别的紧凑特征


随着移动传感器技术的普及，智能可穿戴设备为通过从多维日常传感器信号中学习表达表示来解决具有挑战性的人类活动识别（HAR）问题提供了前所未有的机会。这激励我们开发一种适用于基于摄像头和基于可穿戴传感器的 HAR 系统的新算法。尽管已经报道了竞争性分类准确性，但现有方法经常面临区分由不同时间顺序的活动模式组成的视觉上相似的活动的挑战。在本文中，我们提出了一种新颖的概率算法来对 HAR 活动模式的时间顺序进行紧凑编码。具体来说，该算法学习一组最佳的潜在模式，使其时间结构在识别不同的人类活动中真正发挥作用。然后，引入一种新颖的概率先取全部（pFTA）方法，根据这些潜在模式的顺序生成紧凑特征来编码整个序列，并且可以通过之间的汉明距离有效地测量不同序列之间的时间结构相似性。紧凑的特点。对三个公共 HAR 数据集的实验表明，所提出的 pFTA 方法可以在准确性和效率方面实现具有竞争力的性能。