丰富传感器环境中结构偏差和相关数据的描述
Description of Structural Biases and Associated Data in Sensor-Rich Environments
摘要：
在这篇文章中，我们研究了在传感器丰富的环境中的活动识别。我们解决，特别是，归纳偏见的问题及其对数据汇集过程的影响。为了有效和稳健，活动识别系统必须在各个层面考虑这些偏差，并将其建模为可以控制的超参数。无论是与传感器测量、传输协议、传感器部署拓扑、异质性、动态性或随机效应相关的偏差，重要的是要了解它们对活动识别模型质量的实质性影响。这项研究强调了需要将现实情况中出现的不同类型的偏见分开，以便机器学习模型，例如，适应这些环境的动态性，抵抗传感器故障，并遵循传感器拓扑的演变。我们提出了一个元建模过程中，传感器数据的结构层。较低的层编码与数据的转换、传输和拓扑相关的各种偏差。上层编码与数据本身相关的偏差。这样，就可以更轻松地对超参数进行建模，并跟踪数据采集基础设施中的变化。我们说明了我们的方法SHL数据集，它提供了运动传感器数据的人类活动在真实条件下收集的列表。暴露的权衡和更广泛的影响，我们的方法进行了讨论与替代技术编码和将知识纳入活动识别模型。
英文摘要：
In this article, we study activity recognition in the context of sensor-rich environments. We address, in particular, the problem of inductive biases and their impact on the data collection process. To be effective and robust, activity recognition systems must take these biases into account at all levels and model them as hyperparameters by which they can be controlled. Whether it is a bias related to sensor measurement, transmission protocol, sensor deployment topology, heterogeneity, dynamicity, or stochastic effects, it is important to understand their substantial impact on the quality of activity recognition models. This study highlights the need to separate the different types of biases arising in real situations so that machine learning models, e.g., adapt to the dynamicity of these environments, resist to sensor failures, and follow the evolution of the sensors topology. We propose a metamodeling process in which the sensor data is structured in layers. The lower layers encode the various biases linked to transformations, transmissions, and topology of data. The upper layers encode biases related to the data itself. This way, it becomes easier to model hyperparameters and follow changes in the data acquisition infrastructure. We illustrate our approach on the SHL dataset which provides motion sensor data for a list of human activities collected under real conditions. The trade-offs exposed and the broader implications of our approach are discussed with alternative techniques to encode and incorporate knowledge into activity recognition models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
