ABOUT THE SPEAKER
Jennifer Healey - Research scientist
A research scientist at Intel, Jennifer Healey develops the mobile internet devices of the future.

Why you should listen

Jennifer Healey imagines a future where computers and smartphones are capable of being sensitive to human emotions and where cars are able to talk to each other, and thus keep their drivers away from accidents. A scientist at Intel Corporation Research Labs, she researches devices and systems that would allow for these major innovations.

Healey holds PhD from MIT in electrical engineering and computer science. While there, she pioneered “Affective Computing” with Rosalind Picard and developed the first wearable computer with physiological sensors and a video camera that allows the wearer to track their daily activities and how they feel while doing them. From there, she moved to IBM where she worked on the next generation of multi-modal interactive smartphones and helped architect the "Interaction Mark-Up language" that allows users to switch from voice to speech input seamlessly.

Healey has also used her interest in embedded devices in the field of healthcare. While an instructor at Harvard Medical School and at Beth Israel Deaconess Medical Center, she worked on new ways to use heart rate to predict cardiac health. She then joined HP Research in Cambridge to further develop wearable sensors for health monitoring and continued this research when she joined Intel Digital Health.

More profile about the speaker
Jennifer Healey | Speaker | TED.com
TED@Intel

Jennifer Healey: If cars could talk, accidents might be avoidable

詹妮弗 海丽:汽车若能交流 车祸或可避免

Filmed:
908,454 views

当我们开车是,我们就坐进了一个玻璃气泡中,锁上门,踩下油门,依靠眼睛为我们自己导航。即使我们仅仅只能看见车前车后的几辆车。但是假如汽车能互换关于位置、速度的数据,并运用预测模型来为每一位司机测算最安全的线路呢?詹妮佛 海丽构想了一个零车祸的世界。(在TED@Intel.录制)
- Research scientist
A research scientist at Intel, Jennifer Healey develops the mobile internet devices of the future. Full bio

Double-click the English transcript below to play the video.

00:12
Let's face面对 it:
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让我们来面对一桩事实吧
00:14
Driving驾驶 is dangerous危险.
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开车是一件危险的事
00:17
It's one of the things that we don't like to think about,
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它是我们不愿意去想的事物之一
00:20
but the fact事实 that religious宗教 icons图标 and good luck运气 charms魅力
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但事实上那些神像和平安符
00:23
show显示 up on dashboards仪表板 around the world世界
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世界各地都有人把它们摆在仪表盘的上方
00:28
betrays原形毕露 the fact事实 that we know this to be true真正.
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这就无意中透露出一个我们都心知肚明的事实
00:32
Car汽车 accidents事故 are the leading领导 cause原因 of death死亡
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车祸是死亡的主因
00:36
in people ages年龄 16 to 19 in the United联合的 States状态 --
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尤其在美国16到19岁的美国人群中
00:40
leading领导 cause原因 of death死亡 --
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死亡的主因
00:43
and 75 percent百分 of these accidents事故 have nothing to do
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并且百分之七十五的车祸
00:47
with drugs毒品 or alcohol.
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都与毒品和酒精无关
00:49
So what happens发生?
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那么 究竟发生了什么
00:51
No one can say for sure, but I remember记得 my first accident事故.
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没人能给出确切答案 但我记得我第一次出车祸
00:55
I was a young年轻 driver司机 out on the highway高速公路,
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我当时还是个开车的新手,当时正在外面高速路上开着车。
00:59
and the car汽车 in front面前 of me, I saw the brake制动 lights灯火 go on.
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我突然看见我前面汽车的刹车灯亮了
01:02
I'm like, "Okay, all right, this guy is slowing减缓 down,
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我当时想 好吧 他减速了
01:03
I'll slow down too."
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那我也减速好了
01:05
I step on the brake制动.
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我踩了刹车
01:07
But no, this guy isn't slowing减缓 down.
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但是 我前面那个人并不是在减速
01:09
This guy is stopping停止, dead stop, dead stop on the highway高速公路.
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他(竟然)停车了 突然停车—— 在高速路上突然停车
01:12
It was just going 65 -- to zero?
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速度从65迈瞬降到0
01:15
I slammed抨击 on the brakes刹车.
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我猛踩刹车
01:16
I felt the ABSABS kick in, and the car汽车 is still going,
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我当时感觉到我的车的防抱死系统启动了 但车还在行驶
01:19
and it's not going to stop, and I know it's not going to stop,
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并没停下来的意思 我也知道我的车停不了了
01:22
and the air空气 bag deploys展开时, the car汽车 is totaled总计,
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安全气囊也鼓了起来 车报废了
01:25
and fortunately幸好, no one was hurt伤害.
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但幸运的是 没有人受伤
01:28
But I had no idea理念 that car汽车 was stopping停止,
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但我根本不知道我前面那辆车要停
01:32
and I think we can do a lot better than that.
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而且我想我们可以比那做得更好
01:36
I think we can transform转变 the driving主动 experience经验
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我想我们通过实现“让汽车之间对话”——
01:40
by letting出租 our cars汽车 talk to each other.
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来改变我们的驾驶体验
01:44
I just want you to think a little bit
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我希望你们能思考片刻
01:46
about what the experience经验 of driving主动 is like now.
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思考一下现在的驾驶体验是怎么样的
01:48
Get into your car汽车. Close the door. You're in a glass玻璃 bubble泡沫.
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坐进车里 关上车门 你就已经置身在一个玻璃气泡中
01:53
You can't really directly sense the world世界 around you.
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你不能直接感受到你周围的世界
01:55
You're in this extended扩展 body身体.
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因为你坐在车这样一个不小的空间里
01:58
You're tasked任务 with navigating导航 it down
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你的任务就是导航
02:00
partially-seen部分见过 roadways道路,
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你只能看见道路的一部分
02:02
in and amongst其中包括 other metal金属 giants豪门, at super-human超人类 speeds速度.
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并且以超人类的速度行驶在其他的“金属巨人”间
02:06
Okay? And all you have to guide指南 you are your two eyes眼睛.
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对吧? 你只能靠双眼来导航
02:11
Okay, so that's all you have,
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对 你别无他法
02:12
eyes眼睛 that weren't really designed设计 for this task任务,
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(但其实)眼睛并非是用来干着活儿的
02:14
but then people ask you to do things like,
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但是有些事情你就必须得做,比方说,
02:18
you want to make a lane车道 change更改,
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你想换个车道
02:20
what's the first thing they ask you do?
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那你第一件要做的事是什么
02:22
Take your eyes眼睛 off the road. That's right.
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将眼睛从车道上移开 对
02:25
Stop looking where you're going, turn,
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将目光从你前进的方向移开 转弯
02:27
check your blind spot,
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检查一下盲点
02:29
and drive驾驶 down the road without looking where you're going.
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然后就一直开 根本不注意自己在往哪里开
02:33
You and everyone大家 else其他. This is the safe安全 way to drive驾驶.
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所有人都这么做 这是安全的驾驶方式
02:36
Why do we do this? Because we have to,
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我们为什么这么做? 因为我们别无选择
02:38
we have to make a choice选择, do I look here or do I look here?
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我们必须作出一个抉择 是朝这儿看 还是朝那儿看
02:40
What's more important重要?
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更重要的一点是什么呢
02:42
And usually平时 we do a fantastic奇妙 job工作
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我们通常都做得很好
02:45
picking选择 and choosing选择 what we attend出席 to on the road.
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能够很好的选择我们要往哪个方向开
02:48
But occasionally偶尔 we miss小姐 something.
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但是偶然 我们也会忘记一些事情
02:52
Occasionally偶尔 we sense something wrong错误 or too late晚了.
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有时 当我们发觉有些东西出了问题的时候 已经为时过晚。
02:57
In countless无数 accidents事故, the driver司机 says,
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在无数的车祸中 司机都会说
02:59
"I didn't see it coming未来."
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“我没想到会这样。”
03:01
And I believe that. I believe that.
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我相信他们的话 我相信
03:04
We can only watch so much.
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我们看到的只有这么多而已
03:07
But the technology技术 exists存在 now that can help us improve提高 that.
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但是现存的科技可以帮我们改善这一点
03:12
In the future未来, with cars汽车 exchanging交换 data数据 with each other,
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在未来 车与车之间可以交换数据
03:17
we will be able能够 to see not just three cars汽车 ahead
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我们就不仅只能看见前面的三台车了
03:20
and three cars汽车 behind背后, to the right and left,
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还能看见后面的三台车,左边的,以及右边车。
03:22
all at the same相同 time, bird's鸟类 eye view视图,
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同时看见 鸟瞰视野
03:25
we will actually其实 be able能够 to see into those cars汽车.
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我们可以看见这些车的内部
03:28
We will be able能够 to see the velocity速度 of the car汽车 in front面前 of us,
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我们可以看到我们前面那辆车的速度
03:31
to see how fast快速 that guy's家伙 going or stopping停止.
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看看前面那位什么时候会启动或停下
03:34
If that guy's家伙 going down to zero, I'll know.
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假如那人的速度突然降到零,我就能知道了。
03:38
And with computation计算 and algorithms算法 and predictive预测 models楷模,
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利用运算 代数以及预测模型
03:42
we will be able能够 to see the future未来.
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我们能看见未来
03:46
You may可能 think that's impossible不可能.
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你可能会觉得这是不可能的
03:47
How can you predict预测 the future未来? That's really hard.
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你怎么能预测未来呢 太难了
03:50
Actually其实, no. With cars汽车, it's not impossible不可能.
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事实上 不难 对于汽车来说 这并非不可能
03:54
Cars汽车 are three-dimensional三维 objects对象
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汽车是三维物体
03:56
that have a fixed固定 position位置 and velocity速度.
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位置速度都是固定的
03:59
They travel旅行 down roads道路.
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行使在路上的时候
04:00
Often经常 they travel旅行 on pre-published预发布 routes路线.
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它们通常都是按照预先规划好的道路行驶
04:03
It's really not that hard to make reasonable合理 predictions预测
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未来 想要预测汽车将要驶向什么方向
04:07
about where a car's汽车 going to be in the near future未来.
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对之作出合理的预测并不困难
04:09
Even if, when you're in your car汽车
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即使你在自己的车里
04:11
and some motorcyclist摩托车手 comes -- bshoombshoom! --
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突然出现了一个开着摩托的人 砰!
04:13
85 miles英里 an hour小时 down, lane-splitting车道分割 --
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时速85英里,跟你抢车道
04:16
I know you've had this experience经验 --
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我知道你们曾经有过这样的经历
04:18
that guy didn't "just come out of nowhere无处."
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那个开摩托的并非凭空出现
04:21
That guy's家伙 been on the road probably大概 for the last half hour小时.
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他可能之前的半小时就一直在路上开着吧
04:25
(Laughter笑声)
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(笑声)
04:26
Right? I mean, somebody's某人的 seen看到 him.
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对吧?我的意思是,有人看见过他
04:29
Ten, 20, 30 miles英里 back, someone's谁家 seen看到 that guy,
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在10,20,30英里前,有人看见过他,
04:32
and as soon不久 as one car汽车 sees看到 that guy
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只要一辆车看见那个人
04:34
and puts看跌期权 him on the map地图, he's on the map地图 --
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将他的信息置入地图中,他的位置就会显示在地图上了
04:37
position位置, velocity速度,
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位置,速度
04:39
good estimate估计 he'll地狱 continue继续 going 85 miles英里 an hour小时.
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预测他将保持85英里的时速
04:41
You'll你会 know, because your car汽车 will know, because
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那你就知道了,因为你的车就会知道了,因为
04:43
that other car汽车 will have whispered低声道 something in his ear,
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其他的车已经悄悄把这件事告诉它了
04:46
like, "By the way, five minutes分钟,
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打个比方,“告诉你一声,五分钟之后
04:48
motorcyclist摩托车手, watch out."
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会出现个开摩托的,注意。”
04:50
You can make reasonable合理 predictions预测 about how cars汽车 behave表现.
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你可以对汽车的运动作出合理的预测。
04:53
I mean, they're Newtonian牛顿 objects对象.
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它们可是遵从牛顿力学的物体
04:54
That's very nice不错 about them.
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这是它们的优点
04:57
So how do we get there?
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那么我们怎么样才能做到这一点呢
05:00
We can start开始 with something as simple简单
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我们可以从简单的开始
05:03
as sharing分享 our position位置 data数据 between之间 cars汽车,
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比如在车辆间共享位置数据
05:05
just sharing分享 GPS全球定位系统.
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只需要共享GPS
05:07
If I have a GPS全球定位系统 and a camera相机 in my car汽车,
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假如我的车内装有GPS和摄像头
05:10
I have a pretty漂亮 precise精确 idea理念 of where I am
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我就能清楚地知道自己的位置
05:12
and how fast快速 I'm going.
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自己的速度
05:14
With computer电脑 vision视力, I can estimate估计 where
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那么利用电脑视野,我就可以预测
05:15
the cars汽车 around me are, sort分类 of, and where they're going.
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我周围的车大概在哪里,他们在向哪个方向前进
05:19
And same相同 with the other cars汽车.
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其他的车也是一样
05:20
They can have a precise精确 idea理念 of where they are,
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他们也能知道自己的准确位置
05:22
and sort分类 of a vague模糊 idea理念 of where the other cars汽车 are.
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并且大致知道其他车的位置
05:24
What happens发生 if two cars汽车 share分享 that data数据,
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假如两车共享数据的话会发生什么呢?
05:27
if they talk to each other?
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假如他们实现彼此对话又会发生什么呢?
05:29
I can tell you exactly究竟 what happens发生.
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我可以很明确的告诉你答案
05:32
Both models楷模 improve提高.
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这两种模型都会进步
05:34
Everybody每个人 wins.
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共赢
05:36
Professor教授 Bob短发 Wang and his team球队
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王鲍勃教授和他的团队
05:39
have doneDONE computer电脑 simulations模拟 of what happens发生
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做了个电脑模拟系统
05:42
when fuzzy模糊 estimates估计 combine结合, even in light traffic交通,
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来研究 当我们结合不同的模糊预测的时候会发生什么 即使只是在交通情况通畅、
05:45
when cars汽车 just share分享 GPS全球定位系统 data数据,
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汽车仅仅共享GPS数据时
05:48
and we've我们已经 moved移动 this research研究 out of the computer电脑 simulation模拟
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之后 我们让这项研究不仅限于电脑模拟
05:50
and into robot机器人 test测试 beds that have the actual实际 sensors传感器
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我们还用机器人测试床 它们用到的传感器
05:53
that are in cars汽车 now on these robots机器人:
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正是当下汽车里真正在用到的传感器 在这些机器人上有
05:56
stereo立体声 cameras相机, GPS全球定位系统,
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立体相机 GPS
05:58
and the two-dimensional二维 laser激光 range范围 finders发现者
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二位激光测距仪
06:00
that are common共同 in backup备用 systems系统.
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这些(也)都是备用系统中非常常见的
06:02
We also attach连接 a discrete离散的 short-range短距离 communication通讯 radio无线电,
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我们再安装一个离散短距离无线电
06:07
and the robots机器人 talk to each other.
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实现机器人间的通话
06:09
When these robots机器人 come at each other,
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当这些机器人遇见彼此时
06:10
they track跟踪 each other's其他 position位置 precisely恰恰,
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它们能够准确地追踪彼此的位置
06:13
and they can avoid避免 each other.
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并可躲避彼此
06:16
We're now adding加入 more and more robots机器人 into the mix混合,
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我们现在正在向这样的混合系统中 添加更多的机器人
06:19
and we encountered遇到 some problems问题.
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我们也遇到了一些问题
06:21
One of the problems问题, when you get too much chatter喋喋不休,
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问题之一就是 当(汽车间的)“悄悄话”太泛滥
06:23
it's hard to process处理 all the packets, so you have to prioritize优先,
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就很难处理所有的信息 所以你必须抓重点
06:27
and that's where the predictive预测 model模型 helps帮助 you.
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在这样的情况下预测模型就可以派上用场了
06:29
If your robot机器人 cars汽车 are all tracking追踪 the predicted预料到的 trajectories轨迹,
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假如你的机器人机器正在追踪所有已预测的轨迹
06:33
you don't pay工资 as much attention注意 to those packets.
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那么你就不用花费太多的精力去关注那些了
06:35
You prioritize优先 the one guy
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你可以将重心放在某台
06:37
who seems似乎 to be going a little off course课程.
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看上去有些偏离航向的车上
06:38
That guy could be a problem问题.
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那台车可能是一个隐患
06:41
And you can predict预测 the new trajectory弹道.
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那你就可以预测新的路线
06:44
So you don't only know that he's going off course课程, you know how.
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这样你不仅知道了它正在偏离航向 你还知道了它是怎样偏离的
06:46
And you know which哪一个 drivers司机 you need to alert警报 to get out of the way.
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而且你还能知道你该提醒哪些司机注意躲避
06:50
And we wanted to do -- how can we best最好 alert警报 everyone大家?
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我们一直也想这么做 可是怎样提醒他人才好呢
06:53
How can these cars汽车 whisper耳语, "You need to get out of the way?"
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车怎么可能悄悄给彼此送信 说“你得躲一躲”
06:56
Well, it depends依靠 on two things:
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这个取决于两件事
06:58
one, the ability能力 of the car汽车,
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第一 汽车的能力
07:00
and second第二 the ability能力 of the driver司机.
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第二 司机的能力
07:03
If one guy has a really great car汽车,
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假如一个人的车超棒
07:04
but they're on their phone电话 or, you know, doing something,
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但他在打电话或者,你懂得,开开小差
07:07
they're not probably大概 in the best最好 position位置
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那他可能状态不佳
07:09
to react应对 in an emergency.
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在面对紧急情况的时候措手不及
07:12
So we started开始 a separate分离 line线 of research研究
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所以我们开展了一条独立的研究线路
07:14
doing driver司机 state modeling造型.
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司机状态模型
07:16
And now, using运用 a series系列 of three cameras相机,
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我们利用一系列三个摄像头
07:19
we can detect检测 if a driver司机 is looking forward前锋,
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我们可以监测这个司机是在向前看
07:21
looking away, looking down, on the phone电话,
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向边上看 向下看 打电话
07:24
or having a cup杯子 of coffee咖啡.
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还是喝咖啡
07:27
We can predict预测 the accident事故
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我们能预测车祸
07:29
and we can predict预测 who, which哪一个 cars汽车,
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我们可以预测哪些司机 哪些车
07:33
are in the best最好 position位置 to move移动 out of the way
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能够最快的离开将要出现事故的路线
07:36
to calculate计算 the safest最安全 route路线 for everyone大家.
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为每个人算出最安全的线路
07:39
Fundamentally从根本上, these technologies技术 exist存在 today今天.
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最根本的一点 这些技术现在已经成为了现实
07:44
I think the biggest最大 problem问题 that we face面对
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我认为我们面临的最大的问题
07:47
is our own拥有 willingness愿意 to share分享 our data数据.
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就是我们自己是否愿意分享自己的数据
07:50
I think it's a very disconcerting令人不安 notion概念,
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我认为这是一个非常令人不安的想法
07:52
this idea理念 that our cars汽车 will be watching观看 us,
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设想我们自己的车将要监视着我们
07:55
talking about us to other cars汽车,
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跟其他的车分享我们的一举一动
07:58
that we'll be going down the road in a sea of gossip八卦.
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那样我们就像开车穿过 对我们指指点点的人群
08:02
But I believe it can be doneDONE in a way that protects保护 our privacy隐私,
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但我想这件事可以在保护我们隐私的情况下成功
08:05
just like right now, when I look at your car汽车 from the outside,
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就像现在一样 当我在外面看向你的车的时候
08:09
I don't really know about you.
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我并不能真正的了解你
08:12
If I look at your license执照 plate盘子 number,
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当我看你的车牌号码时
08:13
I don't really know who you are.
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我并不能知道你是谁
08:15
I believe our cars汽车 can talk about us behind背后 our backs.
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我觉得我们的车完全能在我们的背后谈论我们
08:19
(Laughter笑声)
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(笑声)
08:22
And I think it's going to be a great thing.
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而且 我认为这是一件非常好的事
08:25
I want you to consider考虑 for a moment时刻
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我希望你们能思考片刻
08:27
if you really don't want the distracted分心 teenager青少年 behind背后 you
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假如你真心不想让 开在你后面的心不在焉的年轻人
08:31
to know that you're braking制动,
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知道你要停车
08:33
that you're coming未来 to a dead stop.
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你要突然刹车的话
08:36
By sharing分享 our data数据 willingly甘心,
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通过自愿分享我们的数据
08:38
we can do what's best最好 for everyone大家.
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我们可以实现人人共赢
08:41
So let your car汽车 gossip八卦 about you.
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所以让你的车“八卦”你吧
08:44
It's going to make the roads道路 a lot safer更安全.
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这会让我们的道路更安全
08:47
Thank you.
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谢谢大家
08:49
(Applause掌声)
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(鼓掌)
Translated by Minmin Zhu
Reviewed by June He

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ABOUT THE SPEAKER
Jennifer Healey - Research scientist
A research scientist at Intel, Jennifer Healey develops the mobile internet devices of the future.

Why you should listen

Jennifer Healey imagines a future where computers and smartphones are capable of being sensitive to human emotions and where cars are able to talk to each other, and thus keep their drivers away from accidents. A scientist at Intel Corporation Research Labs, she researches devices and systems that would allow for these major innovations.

Healey holds PhD from MIT in electrical engineering and computer science. While there, she pioneered “Affective Computing” with Rosalind Picard and developed the first wearable computer with physiological sensors and a video camera that allows the wearer to track their daily activities and how they feel while doing them. From there, she moved to IBM where she worked on the next generation of multi-modal interactive smartphones and helped architect the "Interaction Mark-Up language" that allows users to switch from voice to speech input seamlessly.

Healey has also used her interest in embedded devices in the field of healthcare. While an instructor at Harvard Medical School and at Beth Israel Deaconess Medical Center, she worked on new ways to use heart rate to predict cardiac health. She then joined HP Research in Cambridge to further develop wearable sensors for health monitoring and continued this research when she joined Intel Digital Health.

More profile about the speaker
Jennifer Healey | Speaker | TED.com

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