Into the latent space of capacitive sensors: interpolation and synthetic data generation using variational autoencoders

M. Monteagudo Honrubia, F.J. Herraiz-Martínez, J. Matanza

Machine Learning: Science and Technology Vol. 6, nº. 1, pp. 015031-1 - 015031-15

Resumen:

For many sensing applications, collecting a large experimental dataset could be a time-consuming and expensive task that can also hinder the implementation of Machine Learning models for analyzing sensor data. Therefore, this paper proposes the generation of synthetic signals through a Variational Autoencoder (VAE) to enlarge a spectra dataset acquired with a capacitive sensor based on a Dielectric Resonator. Trained with signals of several water/glycerine concentrations, this generative model learns the dataset characteristics and builds a representative latent space. Consequently, exploring this latent space is a critical task to control the generation of synthetic signals and interpolating concentrations unmeasured by the sensor. For this reason, this paper proposes a search method based on Bayesian Optimization that automatically explores the latent space. The results show excellent signal reconstruction quality, proving that the VAE architecture can successfully generate realistic synthetic signals from capacitive sensors. In addition, the proposed search method obtains a reasonable interpolation capability by finding latent encodings that generate signals related to the target glycerin concentrations. Moreover, this approach could be extended to other sensing technologies.

Resumen divulgativo:

Recopilar grandes conjuntos de datos adquiridos por sensores puede resultar costoso en tiempo y recursos, lo que limita el desarrollo de modelos de aprendizaje automático en aplicaciones de detección. Nuestra última investigación presenta una solución innovadora: el uso de Variational Autoencoders (VAE) para generar señales sintéticas realistas para sensores capacitivos. Estos modelos aprenden las características clave del conjunto de datos y puede crear nuevas señales a partir de ellas, incluso para concentraciones no medidas. Además, se desarrolló un método de optimización bayesiana para explorar el espacio latente y guiar el proceso de generación.

Palabras Clave: variational autoencoders, latent space search, data augmentation, synthetic data, generative models, capacitive sensors, class interpolation

Índice de impacto JCR y cuartil WoS: 6,300 - Q1 (2023)

Referencia DOI: https://doi.org/10.1088/2632-2153/adb009

Publicado en papel: Marzo 2025.

Publicado on-line: Febrero 2025.

Cita:
M. Monteagudo Honrubia, F.J. Herraiz-Martínez, J. Matanza, Into the latent space of capacitive sensors: interpolation and synthetic data generation using variational autoencoders. Machine Learning: Science and Technology. Vol. 6, nº. 1, pp. 015031-1 - 015031-15, Marzo 2025. [Online: Febrero 2025]