:00:00. > :00:09.Newswatch will be here in 15 minutes' time.
:00:10. > :00:32.Believe it or not, modern nursing as we know it only dates back
:00:33. > :00:34.to the 1800s, to the time of Florence Nightingale
:00:35. > :00:40.The Royal College of Nursing, here in London,
:00:41. > :00:43.is now in its 101st year.
:00:44. > :00:48.For all the life-saving technology that we've seen,
:00:49. > :00:52.the actual act of nursing itself is one relationship that so far has
:00:53. > :01:05.And in the UK, a quarter will be over 65 by 2045.
:01:06. > :01:10.This all means that the pressures on nursing are increasing,
:01:11. > :01:13.and looking after elderly people is becoming a pressing issue
:01:14. > :01:19.Kat Hawkins travelled to Helsinki, in Finland, to discover whether one
:01:20. > :01:29.of these could become the new one of these.
:01:30. > :01:32.I'm here in Helsinki, visiting the home of Marja Roth
:01:33. > :01:38.Hello! Hello, how are you?
:01:39. > :01:47.Nice to meet you! Nice meeting you!
:01:48. > :01:50.She is an ex-air hostess, who likes to keep active
:01:51. > :01:55.Look at the hat as well. That was ages ago!
:01:56. > :01:58.But, after a skiing accident a few years ago, she developed epilepsy.
:01:59. > :02:03.I was unconscious for a little while, then got up and skied,
:02:04. > :02:11.Her epilepsy means she needs daily medication and that her family,
:02:12. > :02:15.who live in New York, want to make sure she's OK.
:02:16. > :02:17.They get this reassurance from her daily nursing visit,
:02:18. > :02:24.Do you think that this is as good as a nursing visit?
:02:25. > :02:27.It's better because they see, actually physical, see me,
:02:28. > :02:31.and then I don't have to wait for somebody to come.
:02:32. > :02:34.They want to check basically that I - ask if I took
:02:35. > :02:36.my pill, and... And just see how you are?
:02:37. > :02:40.How I... Yeah.
:02:41. > :02:43.Face, actually, to see the picture, to see that I'm OK.
:02:44. > :02:46.At the other end of the line is Tuomo Kuivamaki.
:02:47. > :02:48.He is one of the nurses here in Helsinki's first
:02:49. > :02:53.Here, teams of trained nurses each make up to 50 video calls per day
:02:54. > :02:55.to people around the city who need support.
:02:56. > :02:58.So you've still got that kind of real human...
:02:59. > :03:01.And especially some of the older customers, that's like a highlight
:03:02. > :03:05.of the day for them, to have sort of a small chat
:03:06. > :03:14.The hope is that this will cut down on the number of home visits that
:03:15. > :03:17.nurses have to do to people who don't need physical support,
:03:18. > :03:20.freeing up more time for those that do.
:03:21. > :03:22.The software itself, called Video Visit, works much
:03:23. > :03:28.So, while the tech isn't that new, Helsinki is unique in how wisely
:03:29. > :03:31.the government is using it, and that can mean big
:03:32. > :03:34.An in-person nursing visit can cost around 40 euros,
:03:35. > :03:41.but this new type of checkup costs as little as five.
:03:42. > :03:43.And what really comes across, watching this call,
:03:44. > :03:50.And it just shows that that nursing element,
:03:51. > :03:52.that real human connection, is still there, even though it's
:03:53. > :03:56.People do hesitate at technology, and especially in nursing.
:03:57. > :04:00.We are actually taking care of people.
:04:01. > :04:05.It's scary that the robots are coming and taking our jobs.
:04:06. > :04:07.Actually, the robots are in here already,
:04:08. > :04:11.but they are easing our job, and actually giving us the freedom
:04:12. > :04:21.to focus on people who actually need our physical help.
:04:22. > :04:25.Now, medical technologies, of course, are improving
:04:26. > :04:27.One example is the use of wearable technology
:04:28. > :04:31.Now, this can be transformative for people with conditions
:04:32. > :04:33.like facial palsy, Parkinson's and autism,
:04:34. > :04:35.allowing them to control devices remotely, or even
:04:36. > :04:45.My name is Bethan Robertson-Smith, and I'm doing my daily routine.
:04:46. > :04:48.It's a series of exercises to flex the muscles in my face.
:04:49. > :04:52.In 2008, when I was at university studying to be a veterinary nurse,
:04:53. > :05:00.I had a fractured skull, an acquired brain injury,
:05:01. > :05:05.and I was left with facial palsy, also known as facial paralysis.
:05:06. > :05:08.It meant that every one of the 40 muscles that gave expression
:05:09. > :05:20.Years later, I had an operation that allowed me to smile
:05:21. > :05:23.like a Mona Lisa, using just two of the chewing muscles that
:05:24. > :05:28.It's very hard to know exactly what muscles I need to move
:05:29. > :05:39.I came down to Brighton today to try out a new piece of technology that's
:05:40. > :05:42.going to help people like myself, who have got facial palsy.
:05:43. > :05:46.One of the surgeons who operated on me is part of a team of experts
:05:47. > :05:49.developing technologies with sensors to read the muscle activities
:05:50. > :05:58.So, when you were first diagnosed, you had an examination called
:05:59. > :06:01.the needle EMG, where the needle is put into the skin,
:06:02. > :06:04.into the muscles, to read the tiny electrical signals
:06:05. > :06:07.With this technology, what we're using is these sensors
:06:08. > :06:11.So the same kind of reading, but without the pain,
:06:12. > :06:16.You have some degree of crossover between the muscles,
:06:17. > :06:20.and that's why you need the machine learning
:06:21. > :06:24.to interpret which muscle is activating.
:06:25. > :06:28.I'm Sarah Healey, and 30 years ago, I had a brain tumour.
:06:29. > :06:29.Try to raise both eyebrows symmetrically.
:06:30. > :06:35.The operation to take it out left me with paralysis on the right-hand
:06:36. > :06:42.I am certainly not alone, as there are about 100,000 people
:06:43. > :06:47.in the UK who have had facial paralysis for years.
:06:48. > :06:49.So each one of these dots represents the position
:06:50. > :06:54.And so, for example, if you were to try and do
:06:55. > :07:01.And the darker the red, the bigger the signal.
:07:02. > :07:04.So because my left side is better and stronger...
:07:05. > :07:07...it's showing up as stronger on the screen.
:07:08. > :07:15.This is great because for the first time, I'm getting accurate
:07:16. > :07:17.information about what is going on with my face.
:07:18. > :07:23.I tend to overwork this side of my face, so this really
:07:24. > :07:27.is giving me feedback that I have to dampen down the movements I don't
:07:28. > :07:32.want, and this is just so good at doing that.
:07:33. > :07:35.I sort of try and practise in front of a mirror.
:07:36. > :07:37.It's not quite as subtle as this, is it?
:07:38. > :07:40.And also, I'm not that keen on looking in mirrors,
:07:41. > :07:49.This headset takes all the information from sensors,
:07:50. > :07:52.just like in the goggles, but now translates it into real-time
:07:53. > :07:57.Yeah, so I'm trying really hard to make her do a full smile...
:07:58. > :08:04.Doing it to a mirror, you kind of tell yourself
:08:05. > :08:23.Whereas she is like, oh, no, that's not what it looks like.
:08:24. > :08:26.It might sound strange to say, but for the first time
:08:27. > :08:29.since my accident, I'm able to see what my smile actually looks like.
:08:30. > :08:32.Not to make it sound like, I dunno, a strange way,
:08:33. > :08:34.but you're kind of doing it with somebody else.
:08:35. > :08:40.My biggest aim for this would be to be able to help
:08:41. > :08:55.That's been one of my aims for the last 30 years.
:08:56. > :09:03.Have you heard the one about the alien who walks
:09:04. > :09:10.Now, as impressive as this bizarre setup looks,
:09:11. > :09:12.these motion-capture suits and stages are actually the standard
:09:13. > :09:15.way that Industrial Light Magic uses actors to give realistic
:09:16. > :09:16.movements to computer-generated principal characters.
:09:17. > :09:19.No worries! You were very frightening.
:09:20. > :09:25.I mean, he's a nice dad, I think, Jalien.
:09:26. > :09:28.Even the fact that Jalien here is being rendered in real time
:09:29. > :09:30.for the director to see during the performance is not
:09:31. > :09:38.What is brand-new here is the live rendering
:09:39. > :09:46.You know, our big focus was around the face and being able to capture
:09:47. > :09:48.the face at the same time as the body.
:09:49. > :09:51.And we can determine what expressions are happening each
:09:52. > :09:54.frame, and then directors can see that live and make decisions
:09:55. > :09:56.on if the character is working as a character,
:09:57. > :09:59.whether his expressions need to change in terms of the model.
:10:00. > :10:04.In order to process an actor's expressions quickly enough,
:10:05. > :10:12.only one face cam and a few Mo-cap dots are used.
:10:13. > :10:15.This simplified live data is then compared to a higher-resolution 3-D
:10:16. > :10:18.capture of the actor's face that's taken beforehand on a rig called...
:10:19. > :10:25.Now, unlike other facial-capture systems we've seen, which take
:10:26. > :10:28.still images of the actor's face, here they're shooting video
:10:29. > :10:32.of my face moving into and out of each emotion.
:10:33. > :10:34.That means that the facial recreation and the animations
:10:35. > :10:45.The live, high-quality rendering of both face and body can also
:10:46. > :10:48.become a magic mirror on sets, to help the actor to get
:10:49. > :10:52.And I guess it really does make you move differently when you're
:10:53. > :10:55.on set, if you're playing a half-tonne alien,
:10:56. > :10:59.It totally does, as long as I engage my imagination.
:11:00. > :11:01.Because if you can see, I'm totally beautifully...
:11:02. > :11:09.You know, in a way that Jalien can't, my wetsuit moves in a way
:11:10. > :11:17.that maybe that arm and that outfit doesn't move.
:11:18. > :11:28.It's good showing you my, er, my stuff.
:11:29. > :11:32.Don't forget, we live on Facebook and on Twitter...
:11:33. > :11:35.Thanks for having us at your place, Jalien.
:11:36. > :11:38.Now, get out of here! Yeah.
:11:39. > :11:39.Hmm... Out!