Research Session · Analysis 1.0

Accompanied by Dr. André Weinreich, Head of Research & Science from emolyzr/Humboldt University, I’ve run several sessions during the winter semester 2016/17. We now have a gathered some data for the movies which had have enough attendees.

The numerical data provides two main starting points: either analysing the questionaire or the plain heart rate data. We check several options to find some correlations between them. The first obvious result is the relation of mean heart rate and liking. At present we focus on cluster analyses from both “ends”: the average rating as well as the individual pulse.

These are the mean heart rates of each screened movie in alphabetical order. In this earlier post some reasons are given, why these films were chosen. Most movies have been screened in a lecture hall, some at a regular cinema, which clearly caused immersiveness at different levels.

Some clues to read the data:

  • Have a look which approx. mean heart rate the movie evokes
  • Watch for immediate changes (up or down)
  • Look for sections which continously differ from mean heart rate
  • The dynamic range, the film has caused in general, is an indicator as well
  • Can an overall trend be identified in individual sections or the entire film?

American Psycho

Mary Harron · 2000 · n=16 (Berlin, Lecture Hall)

American Psycho Poster

American Psycho Graph


Awakenings

Penny Marshall · 1990 · n=16 (Lemgo), n=14 (Berlin) both Lecture Hall

Awakenings Poster

Awakenings Graph


Deadpool

Tim Miller · 2016 · n=16 (Lemgo, Lecture Hall)

Deadpool Poster

Deadpool Graph


Delicatessen

Jean-Pierre Jeunet & Marc Caro · 1991 · n=14 (Lemgo), n=16 (Berlin) both Lecture Hall

Delicatessen Poster

Delicatessen graph HR mean


Doctor Strange

Scott Derrickson · 2016 · n=16 (Lemgo, Cinema)

Doctor Strange Poster

Dr Strange Graph


Fantastic Beasts and where to Find Them

David Yates · 2016 · n=15 (Lemgo, Cinema)

Fantastic Beasts … Poster

Fantastic Beasts Graph


Gone Girl

David Fincer · 2014 · n=15 (Berlin, Lecture Hall)

Gone Girl Poster

Gone Girl Graph


Heil

Dietrich Brüggemann · 2015 · n=16 (Berlin, Lecture Hall)

Heil Poster

Heil Graph


Her

Spike Jonze · 2013 · n=15 (Berlin, Lecture Hall)

Her Poster

Her Graph


Labor Day

Jason Reitman · 2013 · n=16 (Berlin, Lecture Hall)

Labor Day Poster

Labor Day Graph


Legend

Brian Helgeland · 2015 · n=16 (Lemgo, Lecture Hall)

Legend Poster

Legend Graph


Passengers

Morten Tyldum · 2016 · n=14 (Lemgo, Cinema)

Passengers Poster

Passengers Graph


Robocop

José Padilha · 2014 · n=10 (Lemgo), n=16 (Berlin) both Lecture Hall

Robocop Poster

Robocop Graph


Schönefeld Boulevard

Sylke Enders · 2014 · n=15 (Berlin, Lecture Hall)

Schönefeld Boulevard Poster

Schoenefeld Boulevard Graph


Stereo

Maximilian Erlenwein · 2014 · n=15 (Berlin, Lecture Hall)

Stereo Poster

Stereo Graph


Zootopia

Byron Howard/Rich Moore · 2016 · n=14 (Lemgo, Lecture Hall)

Zootopia Poster

Zoomania Graph


The analysis’ goal is to find some structure in observing the heart rate of movie goers. One outcome might be the relation between the heart rate and the likness. Because we are still in the process of applying different approaches, we are not yet ready to publish the whole data.

If you are interested to get more details of this movie analysis study, it’s progress in analysis, or how Movie Pulse can be used for analyzing any feature length movie yourself, get in touch.

Usability Hint and Bug Report

To all Movie Pulse users: please don’t touch the stop button twice in a short interval. I experienced a time-lag of the interface which affected me to do so. This caused a second recording which will delete the previous one. This affects the current version 1.0.

There might be other required usability improvements or even bugs. Therefore I have prepared a bug-report. Thank you for making this app better. I will work on a improved version.

You are invited to come up with feature request as well!

This is what Movie Pulse tells you

We evaluate your heart rate through a trend line. This approach helps to identify sections of more and less involvement.

There are some ideas for future additions what this measurement can tell – it is a intriguingly process to evaluate our own movie pulses. You are more than welcome to send us (anonymously) your graphs. So we can compare. In addition, feel free to suggest improvements and features you find useful.

See Movie Pulse in Action

It’s a simple task, since the app detects automatically a new recording.

All you have to do is find the movie in the list – tipp: Just type the name, the year will not be helpful, since we search for titles only. But in the list, you’ll find the year helpful to identify the correct one, since movies with identical names exist.