UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

In Defence of ML/CNN for the SensorAble Research Project

Ruttenberg, D; Porayska-Pomsta, K; White, SJ; Holmes, J; (2020) In Defence of ML/CNN for the SensorAble Research Project. OSF Preprints: Charlottesville, VA, USA. Green open access

[thumbnail of Ruttenberg_Research Paper #3--Study #3 -- In Defence of CNN_ML for SensorAble Research.pdf]
Preview
Text
Ruttenberg_Research Paper #3--Study #3 -- In Defence of CNN_ML for SensorAble Research.pdf

Download (655kB) | Preview

Abstract

Animals and humans use a midbrain structure to coordinate and process relevant visual and auditory stimuli while suppressing distracting information. In modelling this assembly and managing both environmental and physiological stimuli using engineering principles, my research aspires to deep learning models that sense, categorize and alert autistic individuals of ecological distractions, biophysical cues and other multimodal input that—left unchecked—could decrease individual focus and increase distractibility and anxiety. The designs that follow are based upon valid and reliable constructs presented in recent, peripherally related research, including: (i) a framework for developing adaptive intelligent user interfaces that enhances user experience (Johnston et al., 2019); and, (ii) convolution neural networks (CNNs) that improve expression recognition through emotion- modulated attention (Barros et al., 2017). My intention is to weave a compelling and explicit rationale as to how and why deep learning models make the most sense when learning tasks derived from image, time-series and text- data and applying these to the SensorAble Research Project.

Type: Working / discussion paper
Title: In Defence of ML/CNN for the SensorAble Research Project
Open access status: An open access version is available from UCL Discovery
DOI: 10.31219/osf.io/4y2hp
Publisher version: https://doi.org/10.31219/osf.io/4y2hp
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Autism Spectrum Condition, Attention, Focus, Machine Language, Convolutional Neural Networks, Cross-Channel Convolutional Neural Networks, Distractibility, Anxiety, Focus, Adaptive Wearable
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media
URI: https://discovery.ucl.ac.uk/id/eprint/10097074
Downloads since deposit
23Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item