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

arXiv: https://arxiv.org/abs/2104.04885 · HTML: https://mlsanity.com/paper/2104.04885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 159 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Label-Free Supervision of Neural Networks with Physics and Domain Knowledge](https://mlsanity.com/paper/1609.05566/) (2016)
- [Weight Agnostic Neural Networks](https://mlsanity.com/paper/1906.04358/) (2019)
- [Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention](https://mlsanity.com/paper/1810.04038/) (2018)
- [Learning Sparse Neural Networks via Sensitivity-Driven Regularization](https://mlsanity.com/paper/1810.11764/) (2018)
- [Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviours](https://mlsanity.com/paper/1812.09113/) (2018)
- [Human Activity Recognition with Convolutional Neural Netowrks](https://mlsanity.com/paper/1906.01935/) (2019)
- [Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery](https://mlsanity.com/paper/2003.01753/) (2020)
- [Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis](https://mlsanity.com/paper/1810.05547/) (2018)
- [Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity](https://mlsanity.com/paper/1810.05596/) (2018)
- [Coverage Protocols for Wireless Sensor Networks: Review and Future Directions](https://mlsanity.com/paper/1901.00511/) (2019)
- [Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues](https://mlsanity.com/paper/1912.00576/) (2019)

## Abstract (arXiv)

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.