Smart Sensor Calibration with Auto-Rotating Perceptrons

Daniel Saromo, Leonardo Bravo, Elizabeth R. Villota

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Sensor calibration is vital to have valid measure-ments of physical activities. Herein we deal withadjusting the signal from a wearable force sensoragainst a reference scale. By using a few samplesand data augmentation, we trained a neural-basedregression model to correct the wearable output.For this task, we tested the novel Auto-RotatingPerceptrons (ARP). We found that a neural ARPmodel with sigmoid activations can outperforman identical neural network based on classic per-ceptrons with sigmoid and even ReLU activation.
Idioma originalEspañol
Título de la publicación alojadaInternational Conference on Machine Learning: LatinX in AI (LXAI) Research Workshop 2020
EstadoPublicada - 1 ene. 2020

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