LIPN-UMR CNRS 7030, Université Sorbonne Paris Nord, 93430 Villetaneuse, France.
Sensors (Basel). 2021 Nov 1;21(21):7278. doi: 10.3390/s21217278.
In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sensing devices, their energy and computational constraints, and their collective (collaborative) dimension. These constraints have a fundamental impact on the final activity recognition models as the quality of the data, its availability, and its reliability, among other things, are not ensured during model deployment in real-world configurations. Current approaches for activity recognition rely on the activity recognition chain which defines several steps that the sensed data undergo: This is an inductive process that involves exploring a hypothesis space to find a theory able to explain the observations. For activity recognition to be effective and robust, this inductive process must consider the constraints at all levels and model them explicitly. Whether it is a bias related to sensor measurement, transmission protocol, sensor deployment topology, heterogeneity, dynamicity, or stochastic effects, it is essential to understand their substantial impact on the quality of the data and ultimately on activity recognition models. This study highlights the need to exhibit the different types of biases arising in real situations so that machine learning models, e.g., can adapt to the dynamicity of these environments, resist sensor failures, and follow the evolution of the sensors' topology. We propose a metamodeling approach in which these biases are specified as hyperparameters that can control the structure of the activity recognition models. Via these hyperparameters, it becomes easier to optimize the inductive processes, reason about them, and incorporate additional knowledge. It also provides a principled strategy to adapt the models to the evolutions of the environment. We illustrate our approach on the SHL dataset, which features motion sensor data for a set of human activities collected in real conditions. The obtained results make a case for the proposed metamodeling approach; noticeably, the robustness gains achieved when the deployed models are confronted with the evolution of the initial sensing configurations. The trade-offs exhibited and the broader implications of the proposed approach are discussed with alternative techniques to encode and incorporate knowledge into activity recognition models.
本文研究了传感器丰富环境中的活动识别。在这些环境中，在数据生成过程的各个层次上会出现许多不同的约束，例如传感设备的固有特性、它们的能量和计算约束，以及它们的集体（协作）维度。这些约束对最终的活动识别模型产生了根本性的影响，因为在实际配置中部署模型时，数据的质量、可用性和可靠性等方面无法得到保证。当前的活动识别方法依赖于活动识别链，该链定义了传感器数据经历的几个步骤：这是一个归纳过程，涉及探索假设空间以找到能够解释观测结果的理论。为了使活动识别有效且稳健，这个归纳过程必须考虑所有层次的约束，并明确地对其进行建模。无论是与传感器测量、传输协议、传感器部署拓扑、异构性、动态性还是随机效应相关的偏差，了解它们对数据质量的实质性影响以及最终对活动识别模型的影响都是至关重要的。本研究强调了需要展示实际情况下出现的不同类型的偏差，以便机器学习模型能够适应这些环境的动态性、抵抗传感器故障并跟踪传感器拓扑的演变。我们提出了一种元建模方法，其中这些偏差被指定为超参数，这些超参数可以控制活动识别模型的结构。通过这些超参数，可以更轻松地优化归纳过程、对其进行推理并纳入附加知识。它还为使模型适应环境的演变提供了一种原则性策略。我们在 SHL 数据集上展示了我们的方法，该数据集包含在实际条件下收集的一组人类活动的运动传感器数据。所获得的结果支持了所提出的元建模方法；值得注意的是，当部署的模型面临初始传感配置的演变时，所获得的稳健性增益。讨论了所提出的方法与替代技术之间的权衡以及将知识编码和纳入活动识别模型的更广泛影响。
