>> ALESSANDRO TIMMI: Good evening, I'm Alessandro Timmi
and under the guidance of Prof. Pennestrì and Prof. Valentini
I developed a methodology for the analysis of the sports motion in Karate.
This methodology was then applied to the elite athletes of the Italian National Karate team.
Before going into the technical details of the thesis, I would like to show you the typical motions
in sports Karate.
In particular, the video we are watching right now, was recorded during the World Championships 2012 in Paris.
We can see that the typical Karate techniques involve both arms and legs,
often in single leg stance.
and are performed at very high speed.
In this sport, however, KO is not admitted: the aim here is to demonstrate that the technique
is very fast and therefore potentially lethal,
without actually injuring the opponent.
Our goal is to improve the execution of these fast and complex motions,
by means of the quantitative assessment of the technique effectiveness.
We also aim to review and optimize the techniques and to identify performance models,
thanks to our collaboration with these elite athletes.
In order to judge a technique, obviously a parameter is required and kinetic energy
appeared to be the most suitable for this purpose,
because it is not only a function of linear and angular speed,
but it also takes into account mass and moments of inertia.
This means that we can objectively judge athletes with different physiques
such as in this photo.
In order to track the human motion, a motion capture system is required.
Ours, in particular, is composed of 8 infrared high-speed cameras
which are arranged as shown in this scheme, on two levels and at the vertices of a square.
These cameras must be calibrated before use.
In order to track the motion, a set of reflective markers must be applied
on the athlete's body: in this case they are 34.
Moreover, before tracking the sports motion the system needs to calculate
the athlete's body proportions,
through a particular posture called "T-pose", which is the one you see in this photograph.
This is the data flow starting from motion capture,
which outputs the markers coordinates as a function of time.
These coordinates are imported into our Virtual Sensei software
and, setting the athlete's weight, height and gender,
we obtain the kinematic and kinetic data of the ahtlete's motion as output.
In addition, we also obtain a 3D animation of a skeleton that mimics the athlete's motion.
Going a little bit deeper into the program details,
let's see what are the functional blocks of Virtual Sensei.
First, we created a multibody model of the human body,
which consists of 15 rigid bodies.
Obviously the hypothesis of rigidity is not actually respected
because the markers applied on the athlete's body are affected by the skin deformability,
consequently the mutual distances between the various markers are not constant over time.
For this reason, to calculate the rotation matrices of the various rigid bodies,
we had to take into account this error introduced by markers
and therefore we needed an algorithm to calculate these rotation matrices anyway.
The chosen algorithm was developed by Gupta and Chutakanonta
and modified by us because originally it supported only rigid bodies characterized by four markers,
while in our case we have some identified by three markers.
Furthermore, in order to calculate masses and moments of inertia
we adopetd some anthropometric tables:
setting weight, height and sex,
they allow obtaining masses, lengths and moments of inertia of the various rigid bodies,
of the various body parts.
Other more common algorithms included in our software program are
Euler parameters, which are used to calculate the angular velocities of rigid bodies,
the norm of a vector, which is used to calculate the linear velocity of the various bodies,
obviously kinetic energy, the weighted average, required to calculate the instantaneous position
of the center of mass, and Mozzi's theorem, used to calculate
the instantaneous position of the rotation axis of the pelvis,
that is very relevant for our purposes, as I'm going to show you later.
So, essentially we obtain the positions of the various rigid bodies and their orientations,
linear and angular velocities, anthropometric parameters,
translational, rotational and total kinetic energy for all the different rigid bodies.
Moreover, we get the kinetic energy for the entire human body,
the position of the center of mass,
the rotation axis of the pelvis and the 3D animation of the skeleton.
This is an example of the skeleton exported by the program.
You can detect the exact position of the center of mass
and its ground projection,
which is very relevant when studying techniques on single leg stance such as a kick.
Now, we are going to see a sample application on an athlete of the Italian national team.
In this case our aim was to see the influence of a soft target pad on the motion efficiency,
when the athlete performs the so-called "Gyaku zuki", that is a reverse punch
performed with the fist opposite to the front leg.
In this video, which is starting shortly, we see the athlete performing the technique on the soft pad.
I'd like to highlight the pelvic rotation:
initially, the hip is "opened", as they say in martial-arts jargon,
and when the technique is completed, the hip is "closed".
Our attention has been mainly focused on three parameters,
i.e. kinetic energy of the whole body,
calculated as the mean of the kinetic energy peaks on a whole series of punches,
the punch speed and the pelvis angular velocity.
In this case we compared the results without and with the soft target pad.
The results say that, with the pad, the athlete develops about 9% more kinetic energy.
This is probably due to the fact that the athlete, having a physical target in front of her,
is more stimulated to give her best and, consequently, to develop more kinetic energy.
The punch speed was roughly equal,
as well as the pelvis angular velocity, but the latter was optimized.
Indeed, looking at the diagrams of the angular velocity
(on the left without the pad, on the right with the pad)
we see that in both of them there is a positive peak of angular velcity
which corresponds to the movement explained before,
that is from "opened" to "closed" hip.
However, without the pad, there is also a movement due to inertia,
i.e. the athlete overtakes the optimal position of the closed hip, goes slightly beyond and must compensate,
i.e. she has to go back and that's why we see that signal oscillating before stabilizing.
On the other hand, with the pad we put a constraint for the athlete, therefore she does not go beyond
and so she gets rid of a movement that is useless for the technique.
Up to now, we have talked about elite athletes, but our aim
was to reach out to a wider audience.
To do this, we had to meet some constraints,
namely developing a version of our product
which was inexpensive both from hardware and software point of view,
which was user-friendly, fast
with short setup, calibration and processing times,
and that could be used in different contexts, such as gym, lab, home.
The answer to these requirements is called Virtual Sensei Lite.
This is a basic version of our program,
that can be freely downloaded from our website
and which is based on Microsoft Kinect tracking system,
that is a sensor developed by Microsoft, initially conceived only for video games.
This sensor allows tracking human motion and thus playing with a console
without having to use a joypad or a keyboard.
The advantages of this sensor are, first of all, its worldwide availability,
which makes it suitable for a program distributed on a website.
It's low cost, it costs about 100 €.
It's a single-camera system,
as compared to the 8 cameras of the mocap system,
it does not require markers on the body nor calibration.
The limit, obviously, is that it is less accurate, less reliable in tracking motions,
because it is strongly affected by occlusions,
i.e. when two limbs of the body are very close or overlapped.
How it works: the Kinect emits a beam of infrared "structured light",
a sort of points grid that is projected onto the scene.
This grid of infrared points, not visible to the naked eye,
is read from the infrared sensor
and, thanks to these data, the depth of the scene in front of it is decoded.
Therefore, it is a sort a 3D sensor.
Subsequently the created 3D map, that you can see here in this box,
is interpreted by means of machine learning,
i.e. the sensor and the libraries which run it have been trained
to quickly compare
thousands of body postures and to compare them with the one seen at that instant.
From this comparison the system detects the correct posture
and extracts the coordinates of the 15 joints
representing the skeleton which mimics the human body.
This is the scheme of how Virtual Sensei Lite works.
It is very similar to Virtual Sensei, but it is simplified,
since we no longer have 15 rigid bodies as input, but we have 15 point masses.
This means that we no longer need to analyze the rotation matrices
because masses are concentrated.
The calculation of anthropometric parameters is identical.
Euler parameters aren't present anymore, of course, and Mozzi's theorem too.
As output we have the same parameters, this time without the rotational kinetic energy
and without the pelvic rotation axis.
This is a very short demo video of the program
which starts with Kinect motion capture.
You can see that the body is clearly separated from the background.
This means that the sensor detected it and attached it a skeleton,
represented by those yellow lines.
The program quickly processes data (here time is real)
and shows a 3D avatar that replicates athlete's motions
and some parameters, i.e. total kinetic energy and linear velocities of hands and feet.
A field test we made on this software program
consisted in having recruited a whole category of young athletes,
just before a competition, and having made them perform the same technique as before, the same punch,
without the soft target pad,
to see if there was a correlation, albeit weak, between competition result
and developed kinetic energy.
Obviously the parameters affecting competition results are several,
however, it is interesting to notice that,
as you can see in this diagram displaying kinetic energy on y-axis
and the final rank on x-axis,
it is interesting to notice that there is a sort of correlation, albeit weak,
of kinetic energy which decreases from the first to the last place in rank.
Therefore, in spite of a large data dispersion,
due to the limited number of tests performed
because of the short available time,
you can see this correlation.
Moreover, the software identified the competition winner, that is the one
who developed the greater kinetic energy among all the participants, about 200 J.
Another interesting application was developed by the Dept. of Mech. Eng. of the University of Cassino.
They developed a Sit-to-stand system,
which allows disabled people to change posture, from sitting to standing.
This system was designed tracking the shoulder joints coordinates
using Virtual Sensei Lite software.
As you can see in this scene, the user is tracked while changing his posture
with our software.
Later, after many trials, an average path of the shoulders was calculated
and this trajectory was used to design an articulated quadrilateral,
that will be shown shortly, which allows changing posture.
This is the system and that is the trajectory obtained through our software application.
So, in conclusion, two systems were developed
which have similar goal and features,
because both allow doing quantitative analyses of fast sports motions.
The former is more suitable for elite athletes and for very accurate analyses
when higher budget and longer time are available.
The latter, the Lite version, instead presupposes shorter times and a considerably smaller budget
and is more suited to a much wider user base.
This is evidenced by the fact that, since the program has been put online,
it has been downloaded about 1000 times worldwide.
As shown, both can be adopted in various fields
other than sports, such as the biomedical one.
Among the future developments, we want to prosecute our studies on elite athletes
to confirm the preliminary results computed so far.
We also aim to develop a database, populated with performance models
extracted from techniques performed by these top level athletes.
Moreover, talking about our Lite program, we want to implement a multi-Kinect
human body tracking, to solve the occlusions issue.
Finally, we aim to maintain our online presence
as done so far with our site virtualsensei and on various social networks.
Thank you for your attention.
[Applause]
PROF. CARLO BRUTTI: So... today the following people,
whose results I'm going to proclaim, sat the final exam
for the Master's Degree in Mechanical Engineering.
Alessandro Timmi, 110 out of 110 cum laude.
[Applause]
So, the official part is over, there is a merry atmosphere
thus I imagine that today and in the days to follow you will celebrate
and looks like you have good reason.
This time I would like to do the conclusive reflection, that is usually done in these situations,
about the fact that this is a day you will remember forever.
It's been a huge number of years and I still remember exactly how I was dressed,
where I was, the speech was made to me ...
and this will happen to you too, because today a transformation carries out.
To put it into words better than mine, I'm going to read a piece by Conrad, who says:
"One goes on recognizing the landmarks of the predecessors, excited, amused,
taking the hard luck and the good luck together
the picturesque common lot that holds so many possibilities for the deserving
or perhaps for the lucky.
Yes. One goes on.
And the time, too, goes on, till one perceives ahead a shadow-line
warning one that the region of early youth, too, must be left behind."
[Applause]