The Rocky Horror Picture Show seen by five study volunteers

“The Rocky Horror Picture Show” · Lecture Record #3

This has been the most “shocking” movie for my students so far. At least three of them could not stand it’s b-movieish style, story and characters. But most of them watched it completely and some of them had have fun. But I assume this has been the minority. The concept of the movie will not work nowadays: to board a loved genre of the former generation (musical) and to whisk contemporary no-no’s, controversial moral issues and sexual frankness into a hym of individuality. My subjective appraisal on this consists of two motivations: 1st – this is not their music, therefore it’s just another musical for them; 2nd – society tends to prefer conservative ideas at present, even the younger are part of this trend. The sexual individuality and freedom of personal choice is still not guaranteed – even 41 years! after RHPS has been released.

We still have not achieved absolute pleasure.

Little Conformity

Although this is still not a valid test scenario in terms of quantity – I’d like to apply the former developed strategies for data comparison to this little data set again. This will help to achieve a valid outcome on a later larger scale of study volunteers.

The Rocky Horror Picture Show · Amount of aberration
The amount of aberration has been quite massive (light grey) and the less than average aberration little (dark grey)

Compared to former records this movie has raised very divers reactions and a small amount of similar tendencies. Just to recall: high values of the light grey depict regions where the subjects tend to have no comparable heart rate. The dark grey regions show the time spans of more similar reactions.

The Rocky Horror Picture Show · Accordance of 100%
Accordance of 100% percentage in red, calculated average in grey.

The absolute identical reactions of alle participants in terms of above or below the base line therefore have been very short and few (8,9 %).

Majority will make the Difference

The Rocky Horror Picture Show · Accordance of majority
The accordance of the majority (3 out of 5) compared with 100% accordance (black)

Five participants can have a majority of 60%. So 3 out of 5 show more similar reactions in high pulse (red) and low pulse (green). The 100% accordance is included in black, since this is a subset of the majority calculation.

Gender Comparison

The Rocky Horror Picture Show · Comparison of gender accordance
Comparison of gender accordance (male blue and female orange)

This comparison is new – almost new. I used it in “The Angry Birds Movie” to differentiate between the parent and the children reactions. I will make this a standard comparison: building subset groups of the study volunteers – the most obvious is gender. As long as we will have a fifty-fifty arrangement. And of course: this is only a valid result, if based on a relevant group size. Having 2 female and 3 male will not really suffice. But for building the tool set, we’ll use it for now. 54,4% of the female and only 28,7% of the male have had identical reactions within their group.

The Rocky Horror Picture Show · Gender difference and accordance
Accordance of all study volunteers (green) and regions where female and male reacted differently compared to the other gender (red)

This will improve the depicting of the differences for sub group comparison: you can easily compare the green amount of the movie, which caused similar reactions on all participants (8,9 %), with the amount of differences between the two groups: red shows the parts (51,7%) where female and male reacted similar within their own group (sex), but different to the other gender.

Visualization

The video depicts again the average threshold of the majority (60% this time) – the image becomes tinted in green and red. To make this work more obvious I have destained the movie beforehand. Additionally the 100% subset of all four test users has been included as well. To distinguish both data visually, a hint for the 100% subset has been added in white. If one image shows a white frame: this means all viewers have reacted the same way, not only the majority. The graph below the timeline shows this subset in white as well.

Citizen Kane · Orson Welles · 1941, Watched and recorded with 4 test persons

“Citizen Kane” · Lecture Record #2

We are one step closer to our general aim. For identifying structures within movies we analyze the heart rate of the viewers with our Movie Pulse app. Because of a generous support we’re now able to record up to 5 viewers in parallel! For a first start with multiple Apple Watches we’ve had four volunteers watching “Citizen Kane”. All Watches are paired to a single iPhone 5 and committed their data without any hassle. The data shows no gaps, missing periods of recording. Only a small exception of being perfect: one record stopped prior the end of the movie. Perhaps the user inadvertently pressed the stop-button. Therefore the last 13 minutes are not having the base of 4 recordings and are less significant.

Having watched “Citizen Kane” is an important requirement in terms of understanding the history of cinema. That motion pictures are able to condense what went wrong in an entire single life with a single ordinary object – this really strikes me every time I see this movie. I don’t want to go into details here. “Rosebud” is all I have to cite.

Raw data of four test persons watching "Citizen Kane" the same time
Raw data of four test persons watching “Citizen Kane” the same time

If we now have a look onto the raw data of our Movie Pulse volunteers – my first reaction has probably been the same than yours: Am I on the right track? For heaven’s sake, how could this mess matter in any meaning?

They all must have seen another movie or the whole Movie Pulse enchilada is for the birds. Wait. The data needed some nurture.

Absolute figures are nonsense

We need to have a look at each recording individually. Each person has his/her own resting heart rate. We don’t know this rate, but we can calculate the overall average for the movie records of this person. This figure along with the minimum and maximum pulse in this period of time is the starting point of all our computations. In the Movie Pulse app there is a far more sophisticated algorithm which computes not only the overall average, but the average changing within a specific time frame. This gives the nice up and down trend line. Having here the plain average will be sufficient for now.

The same data all vertical aligned at their average. Note the absence of absolute figures of the heart rate.
The same data vertical aligned at their average. Note the absence of absolute figures of the heart rate. The colors have been altered to a warm tone for female, cold for male test users.

Adjust the base

What formerly has been the average will now serve as the base – zero null. The numbers of units from average to the lowest will represent the minimum range of units the person has reached. The maximum units are defined the same way above zero. Instead of having a heart rate, we deal with a range of units at a base of zero – the threshold of maximum and minimum our test person has reached at a particular time within the movie.

Amount of deviation

A first attempt is to identify the amount of difference at each recording time. Let’s say at time 0:32:12 person 1 has a value of 4, person 2 a value of 6, person 3 a value of 7 and person 4 a value of minus 4. Three people are above their individual average and one person is below it. The deviation is 4,43…. somehow complicated, therefore computers come in very handy. Two more simple examples: p1 value 1, p2 value 1, p3 value 1 and p4 value 2 will have a deviation of 0,5. Yes 1,1,1,1 will gain 0,0 – no difference at all. Low deviation will mean great conformity – which, in our analysis, is the biggest proof of having viewers responding similar.

Determining the deviation compares the data and will give a guidance: low deviation means users reacted more similar.
Determining the deviation compares the data and will give a guidance: low deviation means users reacted more similar.

To make this graph more readable I added a simple TRUE value for those recordings which have a small deviation (average or below), which is represented by the darker gray. This is a first hint for periods of time which have gained equal reactions.

Direction is all we need

But there is one problem: How about the differences the test users have in terms of range of threshold? Of course, according to my theory, a very high or low value will state either high attention or intense state of being calm. But this is the peak we might come closer soon; for now it’s more elementary and useful at which moments the viewers responded in the same way, the same direction!

The simple overall average of all records is depicted in grey. The red line shows the period of time all four users had have data within the similar area: either above or below their own zero base.
The simple overall average of all records is depicted in grey. The red line shows the period of time all four users had have data within the similar area: either above or below their own zero base.

I came to the conclusion that having an overall average of all four users might not sufficient and will alter the data in a wrong way: Imagine one person reacting completely different. With pure math average his or her data will be taken into account nevertheless it might be completely different than the majority. This is what we’re after: majority.

The resulting graph shows data where the majority (three out of four, 75%) has had similar data either above (red) or below (green) the zero base. 100 % similarity is shown in black.
The resulting graph shows data where the majority (three out of four, 75%) has had similar data, either above (red) or below (green) the zero base. 100 % similarity is shown in black.

For this reason I computed the graph depicting the average raise or descent of A) all four test persons B) of the majority of 75% of the test persons which is shown in red and green. Therefore A) is a subset of B) and is marked in black on this image (white in the video).

Again the movie is boiled down into 2 minutes excepts, placed beside each other; one row representing 30 minutes of the movie.

Those 100% and 75% conformity results have been used in the “Citizen Kane in 2 minutes” movie. The overlay of green and red color is applied at the same rules as the prior published “Metropolis in 2 minutes”: high units above the average will tint the image in red, low units in green. This time the color is multiplied with the image, not overlayed.

The video takes the average threshold of the majority (75%) into account – again the image becomes tinted in green and red. Additionally the 100% subset of all four test users has been included as well. To distinguish both data visually, a hint for the 100% subset has been added in white. If one image shows a white frame: this means all viewers have reacted the same way, not only the majority. The graph below the timeline shows this subset in white as well.

Please remember that this first multiple recording unfortunately is not complete approx. 13 minutes prior the movie ended. So “n” has changed from four to three persons at 1:39:12. There is always room for improvement. Any sugestions are welcome too! 

Metropolis · Fritz Lang · 1927

“Metropolis” · Lecture Record #1

This time I’d like to start to refer to movies which are important in film history and have a certain age. “Metropolis” is the mother of science fiction – besides the Georges Méliès Le voyage dans la lune” (1902!). Numerous experts have already debated about it’s value, impact and whatsoever outcome in vision, theme and cinematic morphology. One thing I find interesting myself is the fact that the author Thea van Harbou has had a different aim than the director Fritz Lang: I remember a statement on the extras of the restored version of Enno Patalas – Lang was mainly fond of the human-machine setting; of machines in general, whereas van Harbou focused the idealistic approach that the heart has a mediator function between hands and the mind.

Metropolis · Fritz Lang · 1927
Metropolis · Fritz Lang · 1927

I showed this movie to my students and picked a random volunteer wearing the Apple Watch for running Movie Pulse for the length of the film.

Again: this is a individual result and can not validate the general impact of the movie.

But beside watching and exploring heart rates of movies myself, giving lectures, thinking of app improvements and features, raising comparable results of the same movie –  I think it’s quite important to achieve some visual experiments on depicting the heart rate graph along with the movie itself. Without legal infringements.

A timeline of "Metropolis" along with the Movie Pulse result of a volunteer.
“Metropolis” timeline along with the Movie Pulse result of a volunteer. (Click to enlarge)

This approach seems very inefficient, besides the less detailed graph and the quite small images. So why not combining the result with the movie itself?

  1. combine the heart rate with the images of the movie
  2. doing this for the length of the movie
  3. depict the change of beeing calm/exited
  4. provide an overview

Those ideas led to the 2 minute movie you can see here:

Example: Finding the sequence at 0:39
1st row = 30 minutes + 2nd row 5th image = minute 0:38 – 0:40

Each row shows 30 minutes of the movie. 2 minutes excerpts are placed side by side, so 15 excerpts are depicting 30 minutes, 4 rows showing the almost 2 hours. The graph below the images is just an orientation – since the green and red tint has been rendered slightly different. I determined an overall average – this refers as base zero for red and green. The max value yields 100 % red, min value provides as 100% green.

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.

Another Inspiration for the Project and the Logo

This movie might be “THE” visual expression what this project “Movie Pulse” is all about – movies have a straight entry point to our emotions: our eyes.
I visited the interfilm festival in Berlin in 2014 and have seen “Par desmit minutem vacates” (engl. “Ten minutes older”) by Herz Frank. This is just an excerpt, because I assume you’re not in a cinema-like surrounding. It’s an audience of almost only children, seeing a movie or theater play (no one really knows, my research told me). Their expressions are so intense and change from one emotion to another in fractions of time. This experience can only be achieved in a cinema/theater, in my honest opinion.

If you know what those children see – please tell me. You can read more about the movie, Neil McGlone wrote an article about it at cinemaofchildhood.

And there was a full length feature movie which inspired me as well.

At the end those little pieces – seen a picture here, watched a movie there and reading various sources are the components for making a decision to build an app like that.

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.