Description of Structural Biases and Associated Data in Sensor-Rich Environments

TL;DR

This paper investigates how various structural biases in sensor-rich environments affect activity recognition and proposes a layered metamodeling approach to better understand and adapt to these biases.

Contribution

It introduces a layered metamodeling process to structure sensor data and biases, improving robustness and adaptability of activity recognition systems.

Findings

Layered structure helps model biases related to data transformations and topology.

The approach improves robustness against sensor failures and environmental changes.

Application on SHL dataset demonstrates practical effectiveness.

Abstract

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.