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TEDxNijmegen

Peter van Manen: Better baby care -- thanks to Formula 1

Peter van Manen: Jinsi mbio za magari zinavyoweza kuwasaidia watoto wachanga?

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Wakati wa mbio za magari za Formula 1, gari linatuma mamia ya mamilioni ya taarifa mbalimbali karakana yake kwa ajili ya uchunguzi na upashanaji taarifa kwa wakati huo huo. Kwa hiyo kwa nini tusitumie mfumo huu wa taarifa sehemu nyingine, kama ... katika hospitali za watoto? Peter Van Manen anatueleza zaidi

- Electronic systems expert
Peter van Manen is the Managing Director of McLaren Electronics, which provides data systems to major motorsports series. Full bio

Motor racing is a funny old business.
Mbio za magari ni biashara ya zamani ya kufurahisha
00:12
We make a new car every year,
Tunatengeneza gari jipya kila mwaka,
00:14
and then we spend the rest of the season
halafu tunatumia msimu wote
00:16
trying to understand what it is we've built
kujaribu kuelewa ni nini ambacho tumekijenga
00:19
to make it better, to make it faster.
kuifanya iwe bora zaidi, na kuifanya iende kasi zaidi.
00:21
And then the next year, we start again.
halafu mwaka unaofuata, tunaanza upya.
00:25
Now, the car you see in front of you is quite complicated.
Gari lililo mbele yako
00:28
The chassis is made up of about 11,000 components,
chesisi inaundwa na vifaa mbalimbali takribani 11,000
00:32
the engine another 6,000,
Injini vingine 6000,
00:36
the electronics about eight and a half thousand.
vya elektroniki takriban 8500
00:38
So there's about 25,000 things there that can go wrong.
kwa hiyo kuna vitu kama 25,000 hivi vinavyoweza kuharibika.
00:41
So motor racing is very much about attention to detail.
kwa umakini wa hali ya juu ni muhimu sana katika mbio za magari
00:46
The other thing about Formula 1 in particular
Kitu kingine kuhusu mbio za magari hasa ya langa langa
00:51
is we're always changing the car.
ni kwamba kila wakati tunabadilisha gari.
00:54
We're always trying to make it faster.
Kila wakati tunajaribu kulifanya bora zaidi.
00:56
So every two weeks, we will be making
Kila baada ya wiki mbili, tunakuwa tunatengeneza
00:58
about 5,000 new components to fit to the car.
vifaa vipya 5000 kwa ajili ya kuweka katika gari.
01:01
Five to 10 percent of the race car
asilimia 5 mpaka 10 ya gari la mbio.
01:05
will be different every two weeks of the year.
linabadilishwa kila baada ya wiki mbili.
01:08
So how do we do that?
Kwa hiyo tunafanyaje haya yote?
01:11
Well, we start our life with the racing car.
Tunaanza na gari la mashindano,
01:14
We have a lot of sensors on the car to measure things.
Tuna vifaa vya kupima mambo mbalimbali vingi sana.
01:17
On the race car in front of you here
katika gari la mashindano mbele
01:21
there are about 120 sensors when it goes into a race.
kuna vifaa vya kupima mambo mbalimbali kama 120 linapoenda mashindanoni.
01:23
It's measuring all sorts of things around the car.
vinapima vitu mbalimbali katika gari
01:26
That data is logged. We're logging about
Taarifa zinawekwa katika kumbukumbu. tunatunza kumbukumbu
01:30
500 different parameters within the data systems,
500 mbalimbali za vitu mbalimbali,
01:32
about 13,000 health parameters and events
vitu mbalimbali kuhusu afya ya gari na matukio
01:36
to say when things are not working the way they should do,
kuelezea mambo yanapoenda vibaya,
01:39
and we're sending that data back to the garage
tunatuma taarifa hizi kwenda kitengo cha matengenezo
01:44
using telemetry at a rate of two to four megabits per second.
Kwa kutumia kifaa cha kupima taarifa mbalimbali
01:47
So during a two-hour race, each car will be sending
kila masaa mawili ya mbio,kila gari linatuma
01:52
750 million numbers.
namba 750 millioni
01:55
That's twice as many numbers as words that each of us
hiyo ni mara mbili ya maneno ambayo
01:57
speaks in a lifetime.
tunazungumza katika maisha yetu
02:00
It's a huge amount of data.
ni kiasi kikubwa cha taarifa.
02:02
But it's not enough just to have data and measure it.
lakini haitoshi tu, kuwa na taarifa na vipimo.
02:05
You need to be able to do something with it.
unahitaji kuwa na uwezo wa kuzifanyia kazi.
02:07
So we've spent a lot of time and effort
Kwa hiyo tumetumia muda mwingi na juhudi
02:09
in turning the data into stories
kubadilisha taarifa kuwa hadithi
02:12
to be able to tell, what's the state of the engine,
ili kuweza kueleza, hali ya injini,
02:14
how are the tires degrading,
Matairi yanachoka vipi,
02:17
what's the situation with fuel consumption?
mafuta yanatumikaje?
02:19
So all of this is taking data
hiyo yote inachukua taarifa
02:23
and turning it into knowledge that we can act upon.
na kuzibadilisha kuwa maarifa tunayoweza kujifunza.
02:26
Okay, so let's have a look at a little bit of data.
Sawa,kwa hiyo tuangalie kidogo kuhusu taarifa.
02:29
Let's pick a bit of data from
tuangalie kiasi kidogo cha taarifa kutoka
02:32
another three-month-old patient.
kwa mgonjwa wa miezi mitatu.
02:34
This is a child, and what you're seeing here is real data,
Huyu ni mtoto, na unachokiona hapa ni taarifa halisi,
02:37
and on the far right-hand side,
na upande huu wa kulia,
02:41
where everything starts getting a little bit catastrophic,
mahali ambapo kila kitu kiaanza kuwa cha hatari,
02:43
that is the patient going into cardiac arrest.
ambapo mgonjwa anapata mshituko wa moyo.
02:46
It was deemed to be an unpredictable event.
inaonekana kuwa ni tukio lisilotabirika..
02:49
This was a heart attack that no one could see coming.
Hili lilikuwa ni shambulio la moyo ambalo hakuna mtu aliyeliona.
02:53
But when we look at the information there,
Lakini tunapoangalia taarifa pale,
02:56
we can see that things are starting to become
tunaona vitu vinaanza kuwa
02:59
a little fuzzy about five minutes or so before the cardiac arrest.
havieleweki kama dakika tano hivi kabla ya shambulio la la moyo.
03:01
We can see small changes
Tunaona mabadiliko madogo
03:05
in things like the heart rate moving.
katika vitu kama mapigo ya moyo
03:07
These were all undetected by normal thresholds
hivi vilikuwa haviwezekani kugundulika na vipimo vya kawaida
03:10
which would be applied to data.
ambazo zitatumika na taarifa
03:12
So the question is, why couldn't we see it?
kwa hiyo swali, lilikuwa ni kwa nini hatukuweza kuona?
03:15
Was this a predictable event?
Je hili lilikuwa ni tukio la kutabirika?
03:18
Can we look more at the patterns in the data
je tunaweza kuangalia tabia za taarifa
03:20
to be able to do things better?
ili kuweza kufanya vitu upya?
03:23
So this is a child,
Kwa hiyo huyu ni mtoto,
03:27
about the same age as the racing car on stage,
umri sawa na gari la mbio jukwaani,
03:29
three months old.
miezi mitatu.
03:33
It's a patient with a heart problem.
Ni mgonjwa mwenye tatizo la moyo
03:34
Now, when you look at some of the data on the screen above,
Ukiangalia baadhi ya taarifa hapo juu,
03:37
things like heart rate, pulse, oxygen, respiration rates,
mapigo ya moyo,oksijeni,upumuaji,
03:40
they're all unusual for a normal child,
vyote sio sawa kwa mtoto wa kawaida,
03:45
but they're quite normal for the child there,
lakini ni sawa kwa mtoto yule,
03:48
and so one of the challenges you have in health care is,
kwa hiyo moja kati ya changamoto tuliyo nayo katika huduma za afya,
03:51
how can I look at the patient in front of me,
Nawezaje kumwangalia mgonjwa mbele yangu
03:55
have something which is specific for her,
na kuwa na kitu maalum kwake
03:58
and be able to detect when things start to change,
na kugundua mambo yanapoanza kubadilika,
04:01
when things start to deteriorate?
mambo yanapoharibika?
04:04
Because like a racing car, any patient,
Kwa sababu kama vile gari la mashindano,mgonjwa yeyote,
04:06
when things start to go bad, you have a short time
mambo yanapoharibika,unakuwa na muda mfupi
04:09
to make a difference.
kuleta mabadiliko.
04:12
So what we did is we took a data system
tulichofanya ni kuchukua mfumo wa taarifa
04:14
which we run every two weeks of the year in Formula 1
ambao unafanya kazi kila baada ya wiki mbili za mwaka
04:17
and we installed it on the hospital computers
na kuufunga katika kompyuta za hospitali
04:20
at Birmingham Children's Hospital.
katika hospitali ya watoto ya Birmingham.
04:23
We streamed data from the bedside instruments
Tulisafirisha taarifa kutoka katika vifaa vya vitandani
04:25
in their pediatric intensive care
katika wodi ya watoto mahututi
04:27
so that we could both look at the data in real time
ili tuweze kuona taarifa kwa wakati huo huo
04:30
and, more importantly, to store the data
na muhimu zaidi,kutunza taarifa
04:33
so that we could start to learn from it.
ili tuweze kujifunza
04:36
And then, we applied an application on top
na baadae tukatumia mfumo
04:39
which would allow us to tease out the patterns in the data
ambao uliruhusu kufanya majaribio na taarifa
04:44
in real time so we could see what was happening,
kw awakati huo huo ili kuona kilichokuwa kinatokea,
04:47
so we could determine when things started to change.
ili tuweze kujua wakati mambo yanapobadilika.
04:50
Now, in motor racing, we're all a little bit ambitious,
katika mbio za magari, tuna kuwa tumejaa matumaini
04:54
audacious, a little bit arrogant sometimes,
na wakati mwingine kujivuna kiasi,
04:58
so we decided we would also look at the children
kwa hiyo tukaamua kuangalia watoto
05:00
as they were being transported to intensive care.
walipokuwa wanapelekwa katika wodi ya watu mahututi.
05:04
Why should we wait until they arrived in the hospital
Kwa nini tusubiri mpaka wanapowasili katika hospitali
05:06
before we started to look?
kabla ya kuanza kuangalia?
05:09
And so we installed a real-time link
Kwa hiyo tukaweka kiunganishi cha wakati huo huo
05:11
between the ambulance and the hospital,
kati ya gari ya wagonjwa na hospitali,
05:14
just using normal 3G telephony to send that data
na kwa kutumia mfumo wa simu wa 3G kutuma taarifa
05:16
so that the ambulance became an extra bed
Kwa hiyo gari ya wagonjwa likawa ni kitanda cha ziada
05:20
in intensive care.
katika wodi ya wagonjwa mahututi.
05:23
And then we started looking at the data.
Na baadae tukaanza kuangalia taarifa.
05:26
So the wiggly lines at the top, all the colors,
Mistari yote hii juu,rangi zote,
05:30
this is the normal sort of data you would see on a monitor --
Ni taarifa za kawaida kuziona katika kirusha picha
05:32
heart rate, pulse, oxygen within the blood,
mapigo ya moyo,oksijeni katika damu,
05:36
and respiration.
na kupumua.
05:39
The lines on the bottom, the blue and the red,
Mistari hapo chini, ya bluu na myekundu
05:42
these are the interesting ones.
hii ni ya kustaajabisha.
05:45
The red line is showing an automated version
Mstari mwekundu unaonyesha mfumo wa moja kwa moja
05:46
of the early warning score
wa maonyo ya mapema
05:49
that Birmingham Children's Hospital were already running.
ambayo yalikuwa yanaendeshwa na hospitali ya watoto ya birmingham
05:51
They'd been running that since 2008,
Walikuwa wanaiendesha toka 2008,
05:53
and already have stopped cardiac arrests
na tayari imesimamamisha mistuko ya moyo
05:56
and distress within the hospital.
na msongo wa mawazo hospitalini
05:58
The blue line is an indication
Mstari wa bluu ni kiashiria
06:01
of when patterns start to change,
cha mwenendo unapoanza kubadilika,
06:03
and immediately, before we even started
na haraka,kabla hata ya kuanza
06:06
putting in clinical interpretation,
na kuweka utafsiri wa kitabibu
06:08
we can see that the data is speaking to us.
tunaweza kuona taarifa zikuzungumza nasi.
06:10
It's telling us that something is going wrong.
zinatuambia kuwa kitu si sawa.
06:13
The plot with the red and the green blobs,
mistari ya rangi nyekundu na kijani
06:16
this is plotting different components
hii inaonyesha kitu kingine
06:20
of the data against each other.
kuhusu taarifa.
06:23
The green is us learning what is normal for that child.
Kijani inaonyesha kilicho sawa kwa mtoto
06:25
We call it the cloud of normality.
tunaiita kuwa ni wingu la kawaida.
06:29
And when things start to change,
na mambo yanapobadilika,
06:32
when conditions start to deteriorate,
na hali kuwa mbaya
06:34
we move into the red line.
tunaenda katika mstari mwekundu.
06:37
There's no rocket science here.
Hakuna kitu cha ajabu hapa.
06:39
It is displaying data that exists already in a different way,
inaonyesha taarifa ambazo zipo katika njia nyingine,
06:41
to amplify it, to provide cues to the doctors,
kuwaonyesha madaktari
06:45
to the nurses, so they can see what's happening.
na manesi, ili wajue kinachoendelea.
06:48
In the same way that a good racing driver
sawa sawa na jinsi dereva wa mbio za magari
06:51
relies on cues to decide when to apply the brakes,
anavyotegemea kama kuna foleni ili ajue wakati wa kupiga breki,
06:54
when to turn into a corner,
wakati wa kukata kona,
06:58
we need to help our physicians and our nurses
tunahitaji kuwasaidia madaktari na manesi wetu
06:59
to see when things are starting to go wrong.
kuona ni wakati gani mambo yanapoanza kuharibika,
07:02
So we have a very ambitious program.
kwa hiyo tuna mpango wa kutia matumaini.
07:06
We think that the race is on to do something differently.
Tunaamini mbio zinaenda kufanya kitu tofauti.
07:09
We are thinking big. It's the right thing to do.
Tunawaza mbali, ni kitu sahihi kabisa kufanyika,
07:14
We have an approach which, if it's successful,
tuna njia ambayo kama itafanikiwa,
07:17
there's no reason why it should stay within a hospital.
hakuna sababu ibaki hospitalini tu.
07:20
It can go beyond the walls.
inaweza kwenda zaidi ya hapo.
07:22
With wireless connectivity these days,
na mawasiliano ya bila waya ya siku hizi,
07:24
there is no reason why patients, doctors and nurses
hakuna sababu wagonjwa,daktari na nesi
07:26
always have to be in the same place
ya kuwafanya wawe sehemu moja
07:30
at the same time.
kwa wakati mmoja
07:32
And meanwhile, we'll take our little three-month-old baby,
na wakati huo huo,tutachukua mtoto wetu wa miezi mitatu,
07:34
keep taking it to the track, keeping it safe,
tutaendelea kumpeleka uwanjani salama,
07:38
and making it faster and better.
na kuifanya kuwa ya haraka na nzuri zaidi.
07:42
Thank you very much.
Asante Sana.
07:44
(Applause)
(Makofi)
07:45
Translated by Joachim Mangilima
Reviewed by Nelson Simfukwe

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About the speaker:

Peter van Manen - Electronic systems expert
Peter van Manen is the Managing Director of McLaren Electronics, which provides data systems to major motorsports series.

Why you should listen

To say that Peter van Manen has a high-speed job would be an understatement. As Managing Director of McLaren Electronics, which provides electronics and data collection software to motorsports events, he and his team work in real time during a race to improve cars on about 500 different parameters. That's about 750 million data points in two hours.

But recently van Manen and his team have been wondering: Why can't the extremely precise and subtle data-collection and analysis systems used in motorsports be applied elsewhere, for the benefit of all? They have applied their systems to ICU units at Birmingham Children's Hospital with real-time analysis that allows them to proactively prevent cardiac arrests. The unit has seen a 25 percent decrease in life-threatening events. And it's just the beginning.

More profile about the speaker
Peter van Manen | Speaker | TED.com