Data Generation Process Modeling for Activity Recognition

Hamidi, Massinissa; Osmani, Aomar

doi:10.1007/978-3-030-67667-4_23

Cited by 7 publications

(13 citation statements)

References 24 publications

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“…The spatial structure of the sensors deployment and the induced views and the phenomena being monitored (human activities in our case) are accentuated by the sensors' capabilities and the perspectives (views) through which the data are collected (position in space, position on the body, video capture modalities, acceleration, gravity, etc.) [23,[31][32][33]. Moreover, the incomplete and redundant perspectives can confuse the concepts between them and reduce the performance of the learning independently of the algorithm used.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The placement of sensors makes it possible to provide various perspectives, and the use of several modalities makes it possible to provide several points of view. Here, the problem is to define the appropriate locations for each modality [23]. The problem is more complex than in the case of non-corporeal deployments because the positions of the sensors between them change according to the movements.…”

Section: Reconciling Various Views/perspectivesmentioning

confidence: 99%

“…The circumference of the circles represents main effects (importance), and the thickness of the edges represents total interaction effects. From [23].…”

Section: Analysis Of the Surrogate Model's Response Surfacementioning

confidence: 99%

“…Organization of the paper. This paper builds upon and extends our previous work on human activity recognition [23][24][25] and is organized as follows. In Section 2, we introduce the context of human activity recognition, the activity recognition chain, and we clarify the scope of our work.…”

Section: Introductionmentioning

confidence: 99%

“…We evaluate a first model based on the traditional activity recognition chain instantiated by using neural networks-based architectures. We then illustrate the dynamic inductive bias selection using the proposed approach based on the optimization of architecture's hyperparameters [23]. Extensive experiments make the case for the proposed meta-modeling approach and show the robustness gains achieved when the deployed models are confronted with evolution of the initial sensing configurations (ablation of an increasing number of sensors from the initial deployment).…”

Section: Introductionmentioning

confidence: 99%

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Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective

Hamidi¹,

Osmani²

2021

Sensors

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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.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Reconciling Various Views/perspectivesmentioning

confidence: 99%

“…The circumference of the circles represents main effects (importance), and the thickness of the edges represents total interaction effects. From [23].…”

Section: Analysis Of the Surrogate Model's Response Surfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations