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.
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.
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.
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.
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.
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.
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 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
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.
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.
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.
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.
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 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.
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.
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.
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 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%).
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.
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.
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.
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!
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.
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!
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.
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.
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?
combine the heart rate with the images of the movie
doing this for the length of the movie
depict the change of beeing calm/exited
provide an overview
Those ideas led to the 2 minute movie you can see here:
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.
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.
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!