Proof of concept study: Testing human volatile organic compounds as tools for age classification of films

Christof Stönner, Achim Edtbauer, Bettina Derstorff, Efstratios Bourtsoukidis, Thomas Klüpfel, Jörg Wicker, Jonathan Williams: Proof of concept study: Testing human volatile organic compounds as tools for age classification of films. In: PLOS One, 13 (10), pp. 1-14, 2018.

Abstract

Humans emit numerous volatile organic compounds (VOCs) through breath and skin. The nature and rate of these emissions are affected by various factors including emotional state. Previous measurements of VOCs and CO2 in a cinema have shown that certain chemicals are reproducibly emitted by audiences reacting to events in a particular film. Using data from films with various age classifications, we have studied the relationship between the emission of multiple VOCs and CO2 and the age classifier (0, 6, 12, and 16) with a view to developing a new chemically based and objective film classification method. We apply a random forest model built with time independent features extracted from the time series of every measured compound, and test predictive capability on subsets of all data. It was found that most compounds were not able to predict all age classifiers reliably, likely reflecting the fact that current classification is based on perceived sensibilities to many factors (e.g. incidences of violence, sex, antisocial behaviour, drug use, and bad language) rather than the visceral biological responses expressed in the data. However, promising results were found for isoprene which reliably predicted 0, 6 and 12 age classifiers for a variety of film genres and audience age groups. Therefore, isoprene emission per person might in future be a valuable aid to national classification boards, or even offer an alternative, objective, metric for rating films based on the reactions of large groups of people.

BibTeX (Download)

@article{Stonner2018,
title = {Proof of concept study: Testing human volatile organic compounds as tools for age classification of films},
author = {Christof Stönner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Klüpfel and Jörg Wicker and Jonathan Williams},
doi = {10.1371/journal.pone.0203044},
year  = {2018},
date = {2018-10-11},
journal = {PLOS One},
volume = {13},
number = {10},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Humans emit numerous volatile organic compounds (VOCs) through breath and skin. The nature and rate of these emissions are affected by various factors including emotional state. Previous measurements of VOCs and CO2 in a cinema have shown that certain chemicals are reproducibly emitted by audiences reacting to events in a particular film. Using data from films with various age classifications, we have studied the relationship between the emission of multiple VOCs and CO2 and the age classifier (0, 6, 12, and 16) with a view to developing a new chemically based and objective film classification method. We apply a random forest model built with time independent features extracted from the time series of every measured compound, and test predictive capability on subsets of all data. It was found that most compounds were not able to predict all age classifiers reliably, likely reflecting the fact that current classification is based on perceived sensibilities to many factors (e.g. incidences of violence, sex, antisocial behaviour, drug use, and bad language) rather than the visceral biological responses expressed in the data. However, promising results were found for isoprene which reliably predicted 0, 6 and 12 age classifiers for a variety of film genres and audience age groups. Therefore, isoprene emission per person might in future be a valuable aid to national classification boards, or even offer an alternative, objective, metric for rating films based on the reactions of large groups of people.},
keywords = {application, atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series},
pubstate = {published},
tppubtype = {article}
}