Distributed Processing of Biosignal-Database for Emotion Recognition with Mahout

by   Varvara Kollia, et al.

This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode. Specifically, we run a random forests classifier on the biosignal-data, which have been pre-processed to form exclusive groups in an unsupervised fashion, on a Cloudera cluster using Mahout. The use of distributed processing significantly reduces the time required for the offline training of the classifier, enabling processing of large physiological datasets through many iterations.



page 1

page 2

page 3

page 4


A Survey on Physiological Signal Based Emotion Recognition

Physiological Signals are the most reliable form of signals for emotion ...

Impact of multiple modalities on emotion recognition: investigation into 3d facial landmarks, action units, and physiological data

To fully understand the complexities of human emotion, the integration o...

Transformer-Based Self-Supervised Learning for Emotion Recognition

In order to exploit representations of time-series signals, such as phys...

EEG-based Emotion Recognition with Spatial and Functional Brain Mapping of CNS and PNS Signals

Emotion plays a significant role in our daily life. Recognition of emoti...

User independent Emotion Recognition with Residual Signal-Image Network

User independent emotion recognition with large scale physiological sign...

Spatiotemporal Networks for Video Emotion Recognition

Our experiment adapts several popular deep learning methods as well as s...

Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devices

Implementing automated emotion recognition on mobile devices could provi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.