The three Finalists were: *WEKIT — Wearable Experience for Knowledge Intensive Training — pitch by Paul Lefrere, Innovation Lead *Sapien Labs (WINNER) — pitch by Tara Thiagarajan, Founder & Chief Scientist *MyndYou — pitch by Shira Yama Nir, Project Manager *Judged by: Bill Tucker, Senior Advisor to the K12 Education Program at the Bill & Melinda Gates Foundation; Eduardo Briceño, CEO and Co-founder of Mindset Works; John Cammack, Angel Investor; Neil Allison, Director of Business Model Innovation at Pearson North America *Álvaro Fernández, CEO and Editor-in-Chief of SharpBrains *Sarah Lenz Lock, Senior Vice President for Policy at AARP and Executive Director of the Global Council on Brain Health (GCBH) *Dr. April Benasich, Director of the Baby Lab at the Rutgers Center for Molecular and Behavioral Neuroscience *Chaired by: Dr. Cori Lathan, Co-Chair of the World Economic Forum’s Council on the Future of Human Enhancement Slidedeck supporting session held during the 2017 SharpBrains Virtual Summit: Brain Health & Enhancement in the Digital Age (December 5-7th). Learn more at: https://sharpbrains.com/summit-2017/

3. Top Brainnovations harnessing Big Data
WEKIT — Wearable
Experience for Knowledge
Intensive Training — pitch
by Paul Lefrere, Innovation
Lead
Sapien Labs — pitch by Tara
Thiagarajan, Founder & Chief
Scientist
MyndYou — pitch by Shira
Yama Nir, Project Manager

4. Judges
Bill Tucker, Senior Advisor to
the K12 Education Program at
the Bill & Melinda Gates
Foundation
Eduardo Briceño, CEO and
Co-founder of Mindset Works
John Cammack, Angel
Investor
Neil Allison, Director of
Business Model Innovation
at Pearson North America

5. * Eg mirror neurons, UCLA Health Sciences: "Study identifies brain
cells that help us learn by watching others: Neurons also fire during
secret glee of schadenfreude.” ScienceDaily, 9 September 2016
IN 1 SLIDE: NOT Mind Meld fantasies
INNOVATIONS: Mirror neurons* with AR-support
We add wearable learning, inspired by neuroscience*, using
multiple senses/sensors, plus big data on: liminal/reflective
learning, tracking mistakes, becoming an expert, forgetting

6. WHO WE ARE
• Dr Paul Lefrere (Innovation, presenter) & Dr Fridolin Wild (CEO)
• Our project’s name is “WEKIT”: Wearable Experience For
Knowledge-Intensive Training
• We began last year, as a $3m “Horizon 2020” R&D project based in
Europe
• Partners include universities, industry (eg helicopters, medical
scanners, space station)
• Collaborators include leaders in the EU’s Key Enabling
Technologies (KET) Ecosystems.

7. WHAT WE DO
• We leverage R&D from multiple domains to augment human capabilities
eg using big data
• We develop wearable AR platforms and sensor combinations to monitor
and help people
• Our new spin-out, WEKIT ECS: “Experience Capturing Solutions”, is to
raise our profile
• We provide the scientific lead for the IEEE VR/AR P2048 WG
• Our work wins Best Paper awards, eg for our Technology Acceptance
Model for VR/AR
• Our goal: a timely scale-out, starting 2018.

8. OUR APPROACH AND SOLUTIONS - 1
• The key to our approach is in the phrase ‘knowledge-intensive’. We
find neglected or intractable problems that arise from knowledge
gaps, eg have been the ‘elephant in the room’ for so long that they
are not noticed, or are accepted as ‘just how life is’.
• An example is ‘feeling confused or bored in class’. Another is ‘senior
moments’.
• We solve such problems using novel and boundary-crossing mixes
of disciplinary knowledge, eg imagine a mix of Technology
Enhanced Learning plus Cognitive Science plus Big Data plus
Complexity Science. That’s what we work with. It can lead to more
affordable, effective, extensible and valued solutions.

9. OUR APPROACH AND SOLUTIONS - 2
• The WEKIT approach is to add wearable body/brain sensors to
today’s AR devices, to allow learners and experts to capture and
then share and literally feel the details of how they perform a task.
This is what we call a ‘Wearable Experience’.
• Applications: performance augmentation; updating; AR add-ons;
teams
• Possible markets: precision brain health; dementia care

10. KPI TIME
• Novelty, Scalability, Research & IP approach, Impact, Sustainability
• Novelty: “It’s always about timing. If it’s too soon, no one
understands. If it’s too late, everyone’s forgotten.” - Anna Wintour
• We’re timely in our novelty; we offer the first viable mass-market
solutions to the challenge of creating highly-personalized, affordable
and memorable AR experiences for people at all stages of life.

11. KPI TIME
• Scalability: Our architecture got top marks for scalability and
interoperability in an external review by our public-sector funders
• Research: Our research collaborators and advisors include Big Data
labs such as the Knowledge Media Institute, and computer science
and neuroscience researchers eg at UCL.

12. KPI TIME
• IP approach: Free IP where this will drive fast take-up and growth.
• Revenue models
• #1: license ways to add personalized Performance Augmentation
(PA) & Tracking to education & training.
• #2: service-based Freemium (eg to track & remedy fall-off in an
individual’s capability/memory/brain health)
• #3: service-based Premium (eg assisted living; real-time avoidance
of high-consequence knowledge gaps)

13. KPI TIME
• Impact: Our open solutions, open standards and open knowledge
are the basis for many social innovations (eg we are partners of the
UK Open University, which is evaluating our learning-experience
model for possible large-scale adoption).
• Sustainability: Our initial funders, the European Union, have
numerous schemes to support highly-rated projects like WEKIT in
‘crossing the chasm’ from the R&D phase to Venture funding. Their
follow-on support has constraints (direct national/state aid to
subsidize sales is not allowed).

14. IN CONCLUSION
• Our focus is on combining Wearable Technology with Augmented
Reality and community- or team-based Big Data, to provide users
(individually or in teams) with new, valued, ethical and evidence-based
ways not only to acquire new knowledge and skills efficiently and
effectively, but also to improve someone’s ability to re-acquire forgotten
capabilities and facts, and to self-manage key aspects of their
environment.
• We have developed an architecture for capturing, editing, recalling and
re-purposing key elements of an individual’s or a group’s professional
or personal experiences and memories. This can provide evidence-
based ways to enhance the speed, accuracy and memorability of AR-
linked techniques for mastering skills and acquiring knowledge.

15. 2017 SharpBrains Virtual SummitDEC 2017

16. 16
AUTOCORRELATION
TIME (MS)
AUTOCORRELATION
TIME (MS)
The need for large scale globally representative normative datasets
If we don’t know what normal looks like, how can we define dysfunction?
1000X DIFFERENCE

17. Metadata standards to
allow cross dataset
comparison
17
Standard sample sizes of 20-50 subjects are
insufficient given large inter and intra-person
variability
2. Are you on any regular medication? Yes No
PleaseList Last Taken1.
2.
3.
Indoors - offic
e
Outdoors - sheltered
Indoors - home Outdoors - naturedominant
Indoors - lab Outdoors - urban dominant
Indoors - other Outdoors - open space
Location of Recording (Select all that apply within any one column):
3. If female, are you currently using oral contraception? Yes No
When was approximatelythedateof your last period? / /
1. Coffe
e
2. Tea
3. Mate
4. Redbull
5. Caffe
i
nat ed Soda (Coke/
Pepsi/MountainDewetc.)
6. Alcohol
7. Cannabis
8. Opium
9. Amphetamines
1. Please indicate if you have you consumed/used any of the following substanses in the last twenty four hours?
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
YesNo hoursago
10. Tranquilizers
11. Acetamenophin
12. Ibuprofen
13. Aspirin
14. SleepingPills
15. Other (Pleasespecify)
16. Other (Pleasespecify)
12. Please check any physical symptoms you are feeling right no w:
1.
2.
3.
Headache Mild 1 2 3 4 5 Severe
Migraine Mild 1 2 3 4 5 Severe
Any kind of pain Mild 1 2 3 4 5 Severe
Nausea Mild 1 2 3 4 5 Severe
Upset Stomach Mild 1 2 3 4 5 Severe
4. How would you describe your overall mood on a scale from very negative to very positive
5. How physically energetic are you feeling right no w?
6. How mentally alert are you feeling right now?
7. How rested are you feeling?
8. How anxious are you feeling?
hours9. How many hours did you sleep last night?
hours ago10. How many hours ago was your last meal?
11. Are you currently experiencing any neur ological or health issues (please list)?
Verynegative VeryPositive1 2 3 4 5 6 7 8 9 10
Verynegative VeryPositive1 2 3 4 5 6 7 8 9 10
Foggy/Unfocused Very Alert1 2 3 4 5 6 7 8 9 10
Verytired Veryrested1 2 3 4 5 6 7 8 9 10
Relaxed Veryanxious1 2 3 4 5 6 7 8 9 10
Subject Name: Email:
Year of Birth: Gender: M F X
SessionStateof MindForm

18. Cleaner, validated data that is not lost when the post doc leaves the lab
Better Data Management
18

19. Expanding the scope of
EEG beyond trained
neuroscientists
19
EEG has become cost effective and portable
but the challenge for many is in analysis

21. Brainbase
Data management and research collaboration
Brainview
EEG analytics tools
Videos and workshops
Bio engineering Physics
Computational
Neuroscience
Neuroscientists Cognitive Scientists
Clinicians
(Neurologists)
Neurolab Partners Citizen Scientists
Datascience
Novel analytical tools to find new structures and patterns in the EEG signal that are relevant to cognition and behavior

24. Intelligent data ingestion to ensure quality

25. Search and compile
datasets across
submissions

26. Tools to help design experiments

33. Thank youDEC 2017

34. ANALYTICS FOR BETTER COGNITIVE CARE

35. Shira Yama Nir
Project Manager | Occupational therapist

36. $818B
People aged 65+ world wide
2010 524M
2060 1.5B
Living with
Mild Cognitive
Impairment
Living with
Alzheimer &
other types
of Dementia
THE NEED
~20%
~10%
An additional 1.1 million home health aides
and nursing assistants will be needed by
2024
Graham, J.
Washington Post, 2017
”
“
?

37. FROM DATA TO ACTIONABLE INSIGHTS
Q
C
8
∑
9
Z
A
%
k
?
;
k
A
k
?
%
k
∑
Care action plan
and therapist
observations
Passively collected voice,
activity, driving and sleep
data
¾
*

38. Setting therapy goals
and assigning training
tasks
ML based
anomalies detected
in day-to- day
activity
General talk
Ask Robert about his
shopping routine,
does he feel any change
lately?
Episodic Memory Training
List the products you need to
buy this week.
Sort the products by the
different sections.
1
2 3
4
5

39. Real Time voice
analysis - generating
actionable insights
during the call
1
2
3

40. 1 2

41. ADULT
DAY
CARE
0
Q
C
8
∑
9
Z
A
%
k?
;
k
A
k
?
%
k ∑
¾
* Q
C
8
∑
9
k
A
%
%
THERAPIST
NETWORK
INDEPENDE
NT /
ASSISTED
LIVING

42. GROWTH STRATEGY
2018 2020 2021 2022
$1.5M
$6.8M
$54M
$96M
$120M
An AI based service used by a
network of care providers
SAAS model
per patient per month

43. THANK YOU!

45. Thank you, Speakers & Participants!

46. Thank you, Summit Sponsors!

47. Thank you, Summit Partners!

48. To learn more, visit sharpbrains.com