  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.
