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Top Brainnovation harnessing Big Data


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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:

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Top Brainnovation harnessing Big Data

  1. 1. 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
  2. 2. 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
  3. 3. * 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
  4. 4. 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.
  5. 5. 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.
  6. 6. 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.
  7. 7. 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
  8. 8. 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.
  9. 9. 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.
  10. 10. 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)
  11. 11. 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).
  12. 12. 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.
  13. 13. 2017 SharpBrains Virtual SummitDEC 2017
  14. 14. 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
  15. 15. 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
  16. 16. Cleaner, validated data that is not lost when the post doc leaves the lab Better Data Management 18
  17. 17. Expanding the scope of EEG beyond trained neuroscientists 19 EEG has become cost effective and portable but the challenge for many is in analysis
  18. 18. 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
  19. 19. Intelligent data ingestion to ensure quality
  20. 20. Search and compile datasets across submissions
  21. 21. Tools to help design experiments
  22. 22. Thank youDEC 2017
  24. 24. Shira Yama Nir Project Manager | Occupational therapist
  25. 25. $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 ” “ ?
  26. 26. 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 *
  27. 27. 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
  28. 28. Real Time voice analysis - generating actionable insights during the call 1 2 3
  29. 29. 1 2
  30. 30. 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
  31. 31. 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
  32. 32. THANK YOU!
  33. 33. Thank you, Speakers & Participants!
  34. 34. Thank you, Summit Sponsors!
  35. 35. Thank you, Summit Partners!
  36. 36. To learn more, visit