I have to admit that I am currently running out of time for watching movies. But a friend helped me out with an impressive movie pulse of “Room” by the Irish Filmmaker Lenny Abrahamson. Brie Larson won the Oscar for her Joy “Ma” Newsome character.

“Room” by Lars Abrahamson, 2015

See by yourself: this is a awesome steady increase- I have never seen something like this before.

I am so curious what kind of story caused this almost symmetrical climax?

My habits on movies are quite special: I avoid to know anything beforehand and just mark a movie “worth watching”, after I picked something here and there. I’m quite sure I will still find a cinema in Berlin playing this movie, it’s on top of my list now!  I won’t even watch the trailer – you may…

Consider sending me your movie pulse after watching this (or any other) movie, since the fun part is comparing.

“Room” by Lenny Abrahamson, Trailer

The Nice Guys · Shane Black 2016

“The Nice Guys”

I am quite busy because of improving my analyzing tool set for multiple records. Spreadheet analysis with charts, graphs, normalization,  gender, age, formulas and stuff. A hell of a lot of data to process. This will keep me busy probably for the next weeks.

But sometimes I am able to reserve some spare time to watch a movie – like “The Nice Guys” by Shane Black which is a really entertaining piece of flick. Steady increasing heart rate – like the joy I had watching it. To guys going goon: Ryan Gosling as the stupid goon and Russell Crowe as the one with the loose fists. Watch those guys in this quite hilarious circumstances yourself. Before it’s to late.


"The Angry Birds Movie" · A Family Record

“The Angry Birds Movie” · A Family Record

We had a long awaited trip to the cinema with our two boys, age 6 the little and the older one became 8 years old just recently. They have watched so many Angry Birds Shorts and now wanted to see the feature movie. When I asked them why, they could not really state more than “it’s fun”. I’ve read a very short review stating it’s a sequence of gags only. Not a good starting point for parents.

But to make an ease start: we’ve had fun watching it. Recording the pulses of all family members had brought some prove that these kind of movies are build on two major layers (if we disregard the fact that we four are not a qualified test screening quantity).

I won’t go into detail on the movie itself. It’s a merely simple world and characters, compared to “Zootopia” for example. But the richness of references to contemporary and history of media has a certain quality. We, the parents, had often laughed out loud: Terence can be seen as a revenant of Chief Bromden from One Flew Over the Cuckoo’s Nest” and you can meet the female twins of “Shining” as well as you can party with Daft Punk. Not to mention that Rick Astley’s “Never Gonna Give You Up” will be played at a central moment for the (former) hero Mighty Eagle. And the main character Red has a interesting personality and a goal; presumably this movie has a story after all.

100% accordance (green) · 100% accordance of the parents (grey) · 100% accordance of the childs (blue) · discrepancy of parents vs. childs (red)
This is my major outcome of inspecting the results of the Family Record: red
depicts the time children and parents reacted different while watching the movieThe following steps will explain how this result has been achieved. If you’re not familiar with the workflow I developed so far, I recommend reading the “Citizen Kane” post first, because I’d like to refer to some steps with not going into detail again.

All four records stored in the Movie Pulse app
All four records stored in the Movie Pulse app

All records as relative values on their main average, which refers as base zero.
All records as relative values on their main average, which refers as base zero
All of the four records of the family members are changed to relative values, based on their single average. Parents records have a warm color, the records of the children are shown in cold tones.

The aberration of all family members (light grey). Higher values show greater discrepance. Dark grey depict areas of less than average aberration.
The aberration of all family members (light grey) and less than average aberration (dark grey)
Higher values show greater discrepancy. Dark grey depict areas of less than average aberration, which means the majority of viewers tend to have the same reactions, either above or below their main average.

Accordance of 100% percentage in red, calculated average in grey.
Accordance of 100% percentage in red, calculated average in grey.
The red line shows the moments of the film when the complete family has reacted in the same manner: all four have had either a raised pulse above zero, or had have a calm pulse below zero. The amount of accordance over time is 17,6%. But I now start a little clustering and interpretation.

The accordance of the majority (3 out of 4) compared with 100% accordance (black)
The accordance of the majority (3 out of 4) compared with 100% accordance (black)
As I stated in the beginning: I refer to the same procedure I developed for “Citizen Kane”: besides the finding of 100% accordance, I am looking for the areas of conformity of the majority of viewers (3 out of 4). This shows a greater pattern of resemblance over time: a high result of 68%!

But this time I will try something new: I’d like to match the differences of the children’s outcome with those of the parents!

The results are divided into the subcategories “Parents” and “Childs” and their results are computed separately. This proofed the idea that watching “The Angry Birds Movie” triggered both groups in different ways.

100% accordance (green) · 100% accordance of the parents (grey) · 100% accordance of the childs (blue) · discrepancy of parents vs. childs (red)
100% accordance
 ·100% accordance of the parents ·100% accordance of the children ·discrepancy of parents vs. childrenThe green row is the result we’ve had in different styles beforehand: 100% accordance (17,6 %) of all family members. Grey is used to mark the timespan when both parents reacted similar (astonishing 57,3%), blue shows the conformity of both boys (an even higher result of 58,8%) and finally red discloses disparity between the parents and children’s reactions (50,4%).

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 time red marks the timespan with higher dynamic of the heart rate, green the area of less change.

“Eddie the Eagle”

To state the two most important things first: you probably will not share the empathy I have towards this movie as long as you have no children yourself. At second: I almost ruined the recording by operating my own app in a wrong way, which humiliates me of course.

I will describe the problem in another post. This movie review is affected by this fact in the type of visual representation of the recorded graph. This time I can not refer to the nice average analysis which displays calm regions in green and exciting areas in red. I could rescue the raw data, at least.

So this time the graph is build from scratch and the rules to identify specific regions differ.

What is important to know before watching this movie: this is based on a real character and real events. Usually I prefer not to know anything of the story of the movie. But this time it is so important to know that Eddie Edwards is based on Michael Edwards who represented Great Britain at the 1988 Winter Olympics in Calgary, Canada. He was the first ski jumper for his country since the year 1929. I knew that. In fact I remember the Olympics of 1988 only because of him. He has been the incarnation of an underdog.

But this has been a long time ago. As I watched the movie I found myself in doubt. Could the real person have been that clumsy? So naive? So determined and dauntless? I even asked myself: How could this happen in real life? But he has been there – I’ve seen him on TV. He was THE story of Winter Olympics 1988. Not to mention the contribution of the president at the closing ceremony: “At these Games, some competitors have won gold, some have broken records, and some of you have even soared like an eagle.

If you don’t know much about the fact that Taron Egerton’s Eddie is based on Michael Edwards you may tent to find him overacting for the first 30 minutes or so. And this movie of course is based on a character and a real story. The writers and the director have used their fictional freedom and in order to compete with the real background, they are not subtile. Not at all.

This movie’s dramaturgy is like fish wrapped in a newspaper. We are aware: the ink might be unhealthy, but fish has been wrapped like this for decades. And finally the fish is delicious.

After struggling with the characterization of Eddie for 30 minutes I have been unable to defend my emotions: this movie made me cry. Despite the obvious ingredients of building a plot with a hero (aim, obstacles, turning points, mentor etc.) – combined with the backstory, I have simply been overrun. As I try to understand why, I came up with the identification I had. Probably my empathy was driven by the parents perspective: having such a encouraged and untalented child the same time. And all doubts finally being proved wrong.

This time red marks the timespan with higher dynamic of the heart rate, green the area of less change.
Exceptional graph: red marks the timespan with higher dynamic of the heart rate, green the area of less change.
Batman v Superman: Dawn of Justice · Zack Snyder · 2016

“Batman v Superman: Dawn of Justice”

This is an obvious kind of movie for affecting the heart rate of the audience. It is another DC variation and it is a Zack Snyder movie. I haven’t had in mind those kind of movies at first hand while developing Movie Pulse. But hey, it is OK. Let’s have a look at this less subtile action movie. Far beyond subtile. Did I mention Zack Snyder?

I don’t want to be part of the bashing, so I hesitate to discuss the ethics of superheroes and just want to state three things.

First: I had have a spectacular time (2:25) in terms of spectacle and time. Honestly.
Second: I really didn’t get why Batman has been ill-disposed towards Superman.
Third: Kabooom!

Batman v Superman: Dawn of Justice · Zack Snyder · 2016
Batman v Superman · Zack Snyder · 2016

I really enjoyed the first half hour of this movie: Zack Snyder played quite brilliant with giving the viewer small pieces of information. This is probably the reason my pulse has such a remarkable raise at the beginning. But by the time I recap: this might be subjective, because Kaboom! has been there as well. And did I mention Hans Zimmer? What a workaholic.

The graph is obviously a remarkable steady increase with few to no times to rest. Discussing this and the peaks add up if compared to other user records.

So, Kabooom anyone?

My 6 year old son got my Apple Watch taped at his arm

“Zootopia” seen by a 6 year old

Convincing my 6 year old son to wear my Apple Watch is easy, because he and his brother love it, but it has been a challenge to get it properly mounted at his arm. The mother has not been around and he did not yet know what Gaffa-Tape stands for.

My sons have seen the trailer with Flash over and over again. And finally here we are, in “Zootopia”. I had have no big expectations and seeing the trailer so many times changed my mood from “this must be funny” to “I got it”. Once more now I am so thankful not to have seen anything else of the film – letting me know the identity of Mr. Big, among so many other surprises, would have spoiled the enjoyment. We burst out laughing  so many times.

At the other hand both of my sons are not yet very familiar with suspense and drama. And seeing the world of movies through the eyes of a 6 year old, will definitely change your perspective on cinema. I remember proudly presenting them “An American Tail” aka “Feivel, der Mauswanderer” as a funny animated highlight. After 7 Minutes or so I had to stop the movie because I had forgotten it starts with a genocide – mouses are convicted by cats and their homes are looted and burned.

My sons recording of "Zootopia", unfortunately the middle part has not been recorded. But it's still a valid source for comparison.
My sons recording unfortunately misses the middle part and is much shorter, since we dropped the credits.

So fear and getting the creeps has been part of “Zootopia” as well, in terms of my sons. The younger told me, tho older won’t tell, but silently gives consent. These reactions can clearly be identified by the large red areas and the constantly increasing heart rate at the whole length of the movie. Too bad that we had somehow a silent part in the middle – probably having the watch not that properly attached. I planned not to use this recording because of the missing middle part – until Alexander, a student of mine, has send his result of the same movie.

If you look at both graphs independently, you would not identify any resemblance at first glance.

The original Movie Pulse of Alexanders visit.
The original Movie Pulse of Alexanders visit. He had watched the movie with complete credits.

He started at the studio fanfare of Disney as we did, but he waited until the very end of the credits. We left at the beginning of them, what explains the different lengths. So I made an overlay and repositioned the graphs with the 1/2 hour scale units to properly match in time. The heart rates are so different concerning the trend line, but while trying to find similarities on comparing red and green areas, I discovered that some of the peaks every 1/2 hour are very close together. One is exactly at the same minute, one differs only a single minute and two are at least only two minutes apart.

These two recordings bear strong resemblance – probably by chance. I am more and more motivated and curious to compare a bigger quantity of Movie Pulses of the same movie.

Astonishing similarities in heart rate while watching "Zootopia" by a 6 and a 25+ year old
“Zootopia” watched by a 6 year old and a young man in the twenties produced great similarities at the high and low peaks.
Hail, Caesar! · Ethan Coen, Joel Coen · 2016

“Hail, Caesar!”

This is a straight entry to the dream factory of Hollywood. It is a movie for those who have spend large parts of their lives in vivid celluloid narratives. For those who are deliberately following a liar who tells doubtful accounts from a fictitious world somewhere, of a scale bigger than life. Definitely for me.

delved into the story of Eddie Mannix and became part of his life as a kind of circus director, whose duty is to manage various show acts, preventing the happening of evil and to solve problems where incidents already occurred. A problem solver, a type of character cinema is full of. But Eddie’s tasks are at an extra layer, because he acts at the root of all main stream movies: he keeps a film studio running. Capitol Pictures is a fake invention of the Coen brothers, as well as all acting actors. “Hail, Caesar” is a mother of all film-in-film movies.

This movie feels so fifties, it looks so perfect in it’s various versions of style – from a musical to a water ballet, a sandal movie as well as a drama (greatly exaggerated by it’s director, played by Ralph Fiennes). Not to mention the Bmovieish western.

I felt the tension while I was accompanying Eddie Mannix on his way through the everyday hassle. Tilda Swinton caused hilarious trouble, playing identical twins whose reputation as columnists are questionable and which are at odds with each other. And this is just one subplot – besides the major issue of hostage-taking Georg Clooney.

I’ve read that this movie is tame, because nothing is at stake. But analyzing my heart rate offers the great participation I had. Look at the second half with it’s large red areas and watch out for the wavy trendline. Unfortunately I can’t tell what caused the green gap after 47 minutes. Again, this is still the weakness: not having the movie at hand to look for the scene which caused it.

I was surprised seeing the end titles – this is it? Wait, Eddy, you still need to solve… no you don’t. With great simplicity and almost unnoticeable the Coen brothers have all loose ends be spun together. Although I would have loved to stay with Josh Brolin and all the others for a while.

Yes, the movie is very artificial. The Coen brothers are at no point trying to blur the boundaries between the fictional story and my life. It is on screen and it stays there. But I was deliberately be part of it. According to the résumé of my seat neighbors – they where not. What did they say? Special humor? I beg your pardon, it’s the Coen brothers.

Hail, Caesar! · Ethan Coen, Joel Coen · 2016
Hail, Caesar! · Ethan Coen, Joel Coen · 2016
Deadpool · Tim Miller · 2016


Spoiler alarm. I can’t write about this movie without referring to it’s, let’s call it – events. It’s not only weapons fired in this movie – the amount of verbal ammunition is by far greater than the number of bullets. In fact the overpaid honk of director, sorry I just cite Tim Millers own film, decided to let Deadpool forget his jam-packed bag of arms in a Taxi. And I am talking about the showdown here.

What happened until then? What did I see? Yes, I laughed in movie which left me clueless.

Deadpools lines sound continuously like to eavesdrop at the locker room in a muscle factory. The male of course. This is tiring an funny at the same time. It comes to great moments of comedy having Deadpool acting with ordinary people which are somehow respected by him, like the taxi driver who drives him not only to the showdown – he already started to get to know Deadpool with us in the beginning, or the old blind! and black lady giving him shelter.

Besides the taxi driver and the blind lady – the opening credits I liked most!

Having Deadpool with it’s opponents is unfortunately cliché and predictable –Ed Skrein is completely the wrong cast for a leader of the somewhat called institution IMHO. Although Tim Miller from time to time let Deadpool dismiss the movie he’s in, but this drowns in the volley of words.

Ryan Reynolds character is so over the top, already before his mutation, so I could not feel with him. But of course, this is the wrong movie looking for feelings.

Looking now at my Movie Pulse, I discover having a hesitating first 1/2 hour, but then it seems like I somehow accompany the hero. I would doubt that “emotionally involved” is suitable here, the movie is full of brutality, albeit exaggerated killing and injuring. This probably explains the larger chunks of red sic! areas in the 2nd and 3rd 1/2 hour. And a boring end with characters so obviously introduced at the beginning – to have some help to bring that to an end. Again – would be interesting having another Movie Pulse to compare with…

Deadpool · Tim Miller · 2016
Deadpool · Tim Miller · 2016