This paper addresses the task of counting human actions of interest using
sensor data from wearable devices. We propose a novel exemplar-based framework,
allowing users to provide exemplars of the actions they want to count by
vocalizing predefined sounds ''one'', ''two'', and ''three''. Our method first
localizes temporal positions of these utterances from the audio sequence. These
positions serve as the basis for identifying exemplars representing the action
class of interest. A similarity map is then computed between the exemplars and
the entire sensor data sequence, which is further fed into a density estimation
module to generate a sequence of estimated density values. Summing these
density values provides the final count. To develop and evaluate our approach,
we introduce a diverse and realistic dataset consisting of real-world data from
37 subjects and 50 action categories, encompassing both sensor and audio data.
The experiments on this dataset demonstrate the viability of the proposed
method in counting instances of actions from new classes and subjects that were
not part of the training data. On average, the discrepancy between the
predicted count and the ground truth value is 7.47, significantly lower than
the errors of the frequency-based and transformer-based methods. Our project,
code and dataset can be found at this https URL

本研究通过使用可穿戴设备的传感器数据来计算人类感兴趣的动作的数量，提出了一种新颖的基于样例的框架，允许用户通过发出预定义声音 ''one''、''two'' 和 ''three'' 来提供所需计数的样例。我们的方法首先从音频序列中定位这些话语的时间位置。这些位置用于识别代表感兴趣的动作类别的样例。然后，在样例和整个传感器数据序列之间计算相似度映射，然后将其输入到密度估计模块中生成一系列估计的密度值。将这些密度值相加即可得到最终的计数。我们引入了一个包含来自 37 个被试者和 50 个动作类别的真实数据的多样化和真实性的数据集来开发和评估我们的方法，其中包括传感器和音频数据。在该数据集上的实验证明了该方法在计算新类和训练数据中不包含的受试者的动作实例时的可行性。平均而言，预测计数与实际值之间的差异为 7.47，显著低于基于频率和基于转换器的方法的误差。本研究的项目、代码和数据集可以在此 https URL 找到。