Propellor FilmTech

Movie Pulse at FilmTech Meetup Berlin

Movie Pulse will be presented with a short pitch at the Propellor FilmTech #5 “BioFeedback/Measuring and Interpretation of Emotions”. Main speakers include Nikos Green, CEO and Founder, Affective Signals · Jens-Uwe Garbas, Group Manager Intelligent Systems, Fraunhofer Institute for Integrated Circuits and Dr. André Weinreich, CEO of Emolyzr.

4th of December 2017 · 7 PM at Kultur Projekte, Klosterstraße 68, 10179 Berlin.


Featured Apple Watch Bezel with shapes of figures

Join Movie Pulse – just share your records

You are probably here, because you recently found Movie Pulse at the web, Facebook or at the iTunes App Store. Hopefully, you are a cinephile person. This is 50% of what’s needed to have fun with this app. The other 50%: you are using iOS and (this is the main obstacle) you must own or have access to an Apple Watch.

Sorry, please stop complaining. I tell you why.

I am a single person, a designer obsessed by movies. And I teach students the use, burden and cool things about media. One topic is film analysis. It’s about the act of learning what do we see, how do we see, read or feel it. And how great masters of cinema history make us become part of the story, make us being immersed.

In short, I developed this app Movie Pulse myself to visualize emotional reactions while watching a movie (preferably at a movie theatre). The iOS app Movie Pulse records your heart rate while you are watching a movie at the cinema. A psychologist and me, we examined recordings of sessions we had conducted with our students. The analysis will still take a while, but you can read about the intermediate results here.

Graph with regular samples
A plain heart rate record would look like this
It · a user record
A user record by Movie Pulse looks like this · “It” 2017 by Andy Muschietti

Take it for free, but share the results

I am interested to see your results, those of the cinephile or movie obsessed users and want to see looooooooots of graphs of any genre. So people can compare and discuss the differences and similarities by themselves. This is the reason I no longer charge for this app. It is available for free at the Apple App Store. Enjoy.

Movie Pulse is the only film tool offering you a timeline with your emotional reactions! Your physiological body reactions are the most objective sources.

Wouldn’t it be nice to see these records clustered? Send your graphs along with a personal note, state what you liked or disliked. I won’t mention your name, just initials and the country of origin. Therefore I politely ask you to send your graphs either:

  • by e-Mail,
  • offering a URL to pin the graphs at Movie Pulse Pinterest board or
  • you can even post them on Movie Pulse Facebook page yourself.

Feel free to place your recorded graphs anywhere on the internet, just mention the app. So others have the chance to use the app for their personal analysis.

Some additional information you might need, bundled at the end for your convenience:

May the pulse be with you.
Yours, Heizo Schulze

How can we improve Movie Pulse?

Almost off for summer vacation, we’d like to leave this survey [completed] for those of you interested in Movie Pulse. We love to hear how we can improve the app.

We’ve outlined several ideas for additional features in the survey and we are really curious what do you think. No matter if you are new to Movie Pulse or if you are a regular “recorder”. So, come on: spend less than 5 minutes.

Help improving Movie Pulse answering some questions.

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


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

Awakenings Poster

Awakenings Graph


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

Deadpool Poster

Deadpool Graph


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


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

Heil Poster

Heil Graph


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


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

Legend Poster

Legend Graph


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

Passengers Poster

Passengers Graph


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


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

Stereo Poster

Stereo Graph


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.

Movie Pulse at the cinema

The 2016/17 Research Sessions

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 had up to two groups each week – one at the University Ostwestfalen-Lippe in Lemgo, the second at Humboldt University, Berlin. André Weinreich is a psychologist and researcher of emotions. He instantly understood what I am after, to examine the recorded heart rate of movie goers and he urged me to collect data on a broad variation of movie genres. We have two more movies left, but I’d like to compile the list of the recent movies.

The data is about to be analyzed – which will keep us busy the next months.

We probably release some insights from time to time, but please be patient for the proven outcome. Nevertheless I am still working on Movie Pulse as a tool useful for movie maniacs. I have a certain feature in mind for the next major release, but you’re always welcome to send ideas, feature requests and of course report malfunctions.

Autumn/Winter 2016/17 · Research Sessions

  • The Tin Drum, Volker Schlöndorff · 1979
    n=14 (Lemgo)

This story is based on a novel which is freely inspired by historical events. I know this is the most controversy movie I show in my lessons, because students of several years told me. It has been chosen because it evokes disgust as well as sexual stimulation (among other other things, of course).

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

A drama with the focus on the relationship of a doctor and one of his patients, based on real events. This has been the movie I cried the most in my whole life. It has been chosen because of it’s emotional power to evoke compassion as well as it comes with a particular humor.

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

A caretaker is threatened by the household. Although a bit outdated in terms of it’s fantastic and dystopian filmic universe, the movie is a classic and has been a boiler plate for a series of these kind of movies. It’s part of the screening because of a provocative story, an imminent bad ending and hilarious comedy.

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

A mainstream movie, based on the Marvel comic universe with a favorable actor. Part of the list because of the absence of real human being living condition to identify with, as well as the presence of FX in visual and audio terms.

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

Based on the life of two real brothers, this movie brings two important things: an impressive performance of a famous actor (playing adult twins) along with an incredible story. Chosen because of it’s intensity of character presence, conflict and brutality.

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

An intellectual movie facing a near future we might have with our smart devices. The movie develops an relationship between the main character and a machine. The scale of emotional involvement of the audience in this artificial plot setup makes this movie interesting for the list.

  • Phantastic Beasts and where to Find Them, David Yates · 2016
    n=15 (Lemgo, Cinema)

A movie based on the proven success of an existing phantasmagorial filmic universe. It deals with conflicts of characters of a non-contemporary plot, fantastic creatures and audio-visual impression. It has been chosen because of the probably lesser character identification, but larger impact in visual and sound terms.

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

Based on a major conflict a couple has to deal with, the story heads towards classic changes in character judgement by the audience. This movie delivers a focus on the compassion the audience might have with the male or female – without being distracted by visual effects.

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

The dystopian Science Fiction settled on Earth in some near future re-interprets the successful original from 1987. The movie contains threatening cutting edge technology as well as the misfortune of a human being. Both made this movie useful for the list: the massive audiovisual impact of FX and the sympathy with the main character.

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

Based on another Marvel comic character, this movie provides a cynical, obscene and hilarious hero and a classic hero arc. This movie has been chosen because of it’s classical plot, hilarious comedy elements as well as it’s controversial character.

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

A female misfit character reclaims her acceptance within her adolescence development. A movie dealing with down-to-earth real problems of a young woman in a contemporary time frame and a ordinary setting. The absence of larger than life plot construction (and production scale) makes this movie of use for the list. How will this affect the compassion ability of the audience?

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

A weak character by definition (bunny) in a classical zero to hero (animation) movie. How will the animated characters move the emotions of the audience? Will the movie provide an immersive experience similar to non-animated movies? These are some main questions we might have analyzing it.

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

Preceding events of the life of the main character are affecting the love to a woman. The movie comes with a realistic setting along with a kind of psychedelic split-person threat. How does the audience deals with the psychic harassment as well as with the imminence through physical brutality will make this movie of use for us.

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

Besides it’s humorous handling of (German) nationalism, the movie has an intellectual quality while offering jokes on all kind of institutions, social levels as well as media formats. The questions for the study can contain the following: How does the controversial topic, the hilarious exposure of quite all characters and the quick succession of jokes will affect the audience?

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

A unpredictable character intrudes the life of a single mother with light force. A drama as well as a love story does the movie provide a strong quality of sympathy for all three main characters: intruder, mother and son. Besides the grade of identification with the personell will (presumably) the predictable and imminent end provide strong emotional response of the audience.

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

This science fiction with a fundamental optimistic belief in human expansion (technological and spacial) is an intimate theater on a space ship of sheer enormity. The lack of an alien threat helps to focus on the existential situation (mainly) two people are in. Therefore we can exploit how the audience involves with the development of the couple and how the the imminence both have to deal with affects visually and especially at the sound design.

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

Summer 2016 · Research Sessions

We probably won’t be able to include the following movies, because of poor attendance and we had an essential change in questionnaire structure. We might be able to run additional sessions to increase the required number of test persons; the sessions are not necessarily bound to take place at the same location/at the same time. But, the lack of questionnaire quality does not recommend it.

Guardians of the Galaxy, James Gunn · 2014
n=8 (Lemgo)

Victoria, Sebastian Schipper · 2015
n=12 (Lemgo)

A Fish called Wanda, Charles Crichton/John Cleese · 1988
n=8 (Lemgo)

Fight Club, David Fincher · 1999
n=7 (Lemgo)

Martyrs, Pascal Laugier · 2008
n=7 (Lemgo)

"Avakenings" 1990 by Penny Marshall · Normalized raw data - not analyzed yet

New Research Partner · Summer Break

I am very happy about our new very interesting research connection: Dr. André Weinreich, Head of Research & Science from emolyzr (DE) is joining our Movie Pulse research group. He is a psychologist and dedicated researcher of emotions. His particular interest to join is the supposed ability to find emotionial patterns in peoples reactions, while watching movies  – with the help of our app. This would be a breakthrough, because the app frees the research process from the need of a lab (within the limits of data which can beachieved with a wearable, of course). You can find more about their very sophisticated approach if you watch the videos of this company, which has been founded by the Humboldt University, Berlin.

Although some more data is waiting to be analyzed – we will now have our holiday break. Enjoy your days of lazyness or activity…

We will continue to analyze already recorded data, as well as we will run new screenings. I am especially keen to see the results of “Awakenings” 1990 by Penny Marshall – one of the most emotionial movies I’ve ever seen.

"Avakenings" 1990 by Penny Marshall · Normalized raw data - not analyzed yet
“Awakenings” 1990 by Penny Marshall · Average raw & normalized – not yet analyzed

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.


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! 

“Pace in the Movies” by David Wark Griffith, published in The Liberty Magazine in 1926

David W. Griffith refers 1926 to Pulse in Motion Pictures

There are some serious attempts to deal with the fact that pace in movies has some impact of how a movie succeeds, in dramaturgical terms. I’d like to introduce an article by Mike Baxter, Daria Khitrova, Yuri Tsivian: „A Numerate Film History? Cinemetrics Looks at Griffith, Griffith Looks at Cinemetrics“. Yuri Tsivian and his colleagues found a very old proof that the topic I am devoted to – Movie Pulse – has been in discussion already 90 years ago. The mentioned article of the people above is dedicated to find similarities and points of contact for their own work:, a database for analyzing the montage of movies.

But as Yuri Tsivian introduced me the little-known essay “Pace in the Movies” by David Wark Griffith, published in The Liberty Magazine in 1926, I could hardly believe what I am seeing and reading: A woman trying to get her average heart beat while watching a movie, captioned: „Fans will find it fascinating to measure their pulse beats with the pacing of motion pictures.“ Baxter, Khitrova and Tsivian even considered it might be a hoax or at least an essay from a pretender, but no. It’s written by David W. Griffith.

“Pace in the Movies” by David Wark Griffith, published in The Liberty Magazine in 1926
“Pace in the Movies” by David Wark Griffith · Fans will find it fascinating to measure their pulse beats with the pacing of motion pictures.

Griffith explains the relation of the heart beat and pace, which he refers to as „the ebb and flow of pleasurable tides of excitement, the rhythmical movement of events toward the ecstatic consummation of romantic and adventurous dreams“. He makes several attempts to explain the formal movie structure of sequential images and their narrative structure in sequences, scenes and shots to make the readers aware of the need and use of different pace. And he does not only focusses on various climax’ and increase only; he refers to slow down and retardation as well.

At the end of the essay he encourages the readers to make a self-experiment to measure their own pulse while watching certain scenes in a movie. „You will find that it is, for the very good reason that the whole science of pace in the drama is founded upon your pulse.“ Well said (in 1926). Although today I strongly disagree to do it at the second screening – you can do it instantly with Movie Pulse and a Apple Watch. Just press „start“ at the studio fanfare.