The Internet of Things means not just that computing devices have connectivity to the cloud but that they themselves are connected to each other, and therefore that novel applications can be developed in this rich ecosystem. One area for development is linking quantified self wearable sensors with automotive sensors for applications including Fatigue Detection, Real-time Parking and Assistance, Anger/Stress Reduction, Keyless Authentication, and DIY Diagnostics.

1. September 4, 2014
Ann Arbor, MI USA
Slides: http://slideshare.net/LaBlogga
Melanie Swan
m@melanieswan.com
Sensor Ubiquity:
Blockchain Tech & Automotive-Quantified Self
Integrated Sensor Applications
Image credit: M. Ghezel

2. About Melanie Swan
 Technology Philosopher and Startup Founder
 Instructor at Singularity University, Affiliate
Scholar at IEET, Invited Contributor to EDGE
 Education: MA Candidate Kingston University
London and Université Paris 8, MBA Finance,
Wharton, University of Pennsylvania; BA
French/Economics, Georgetown University
 Corporate Experience: Fidelity, JP Morgan,
iPass, RHK/Ovum, Arthur Andersen
 Sample publications:
 Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science.
 Swan, M. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the
Quantified Self 2.0. J Sens Actuator Netw 2012, 1(3), 217-253.
 Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified
Self, and the Participatory Biocitizen. J Pers Med 2012, 2(3), 93-118.
 Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704.
 Swan, M. Emerging Patient-driven Health Care Models: an Examination of Health Social Networks,
Consumer Personalized Medicine, and Quantified Self-tracking. Int J Environ Res Public Health 2009, 2,
492-525.
Source: http://melanieswan.com/publications.htm
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Automotive-QS Sensors 1

3. Agenda
 Introduction to Quantified Self
 Automotive-QS Sensor Top 5 Killer Apps
 Fatigue Detection
 Real-time Parking and Assistance
 Anger/Stress Reduction
 Keyless Authentication
 DIY Diagnostics
 Conclusion
 Risks
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4. What is the Quantified Self?
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 Individual engaged in tracking any kind
of biological, physical, or behavioral
data about him/herself
 Key assumption: access to information
means the ability to take action
 Self-tracking goal: optimality and
quality-of-life-improvement (vs.
pathology resolution)
 Seeking highly personalized intervention for
sleep quality, depression, low energy,
cognitive alertness, productivity
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological
Discovery. Big Data June 2013, 1(2): 85-99.

5. Quantified Self Global Community
 “Self knowledge through numbers”
 Show-and-tell meetups (100+), videos,
and conferences
1. What did you do?
2. How did you do it?
3. What did you learn?
 Quantified data acquisition through
technology (i.e.; wearable sensors,
mobile apps, other QS devices)
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http://quantifiedself.com/
Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012.

6. Increasingly continuous and automated data collection
Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre), Scanaflo Urinalysis1
Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API
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(Sano Intelligence), Continuous Monitors (Medtronic)
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QS Sensor Mania! Wearable Electronics
Source: Swan, M. Sensor Mania! J Sens Actuator Netw 2012.
1Glucose, protein, leukocytes, nitrates, blood, bilirubin, urobilinogen, specific gravity, and pH urinalysis

7. Types of Quantified Self Measures
 Physical Activities
 Miles, steps, calories, repetitions, sets, METs1
 Diet and Nutrition
 Calories consumed, carbs, fat, protein, specific ingredients, glycemic index,
satiety, portions, supplement doses, tastiness, cost, location
 Psychological, Mental, and Cognitive States and Traits
 Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence
 IQ, alertness, focus, selective/sustained/divided attention, reaction, memory,
verbal fluency, patience, creativity, reasoning, psychomotor vigilance
 Environmental Variables
 Location, architecture, weather, noise, pollution, clutter, light, season
 Situational Variables
 Context, situation, gratification of situation, time of day, day of week
 Social Variables
 Influence, trust, charisma, karma, current role/status in the group or social network
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1METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-that-perfect-
quantified-self-app-notes-to-developers-and-qs-community-html/

8. The Quantified Self is Mainstream
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 QS Device (Fitbit, Glass, etc.) Market: $5 bn by 20161
 Self-tracking statistics (Pew Research Center)
 60% US adults track weight, diet, or exercise
 33% US adults monitor blood sugar, blood pressure,
headaches, or sleep patterns
 9% receive text message health alerts
 40,000 smartphone health applications
 QS thought leadership
 Press : BBC, Forbes, and Vanity Fair
 Electronics show focus at CES 2013
 Health 2.0: “500+ companies making
self-management tools; VC funding up 20%”
1Gartner, 2014, http://www.nbcnews.com/tech/innovation/data-overload-quantified-self-really-future-n189596
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.
Big Data June 2013, 1(2): 85-99.

9. Quantified Self - Ann Arbor
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Contact: Spencer Covey
(SpencerCovey@Gmail.com)
http://www.meetup.com/Quantified-Self-Ann-Arbor/

10. Quantified Self - Tokyo
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Automotive-QS Sensors
Contact: Takashi Kido, RIKEN
(Kido.Takashi@gmail.com)
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http://qs-tokyo.com/
http://www.meetup.com/Quantified-Self-Tokyo/

11. Agenda
 Introduction to Quantified Self
 Automotive-QS Sensor Top 5 Killer Apps
 Fatigue Detection
 Real-time Parking and Assistance
 Anger/Stress Reduction
 Keyless Authentication
 DIY Diagnostics
 Conclusion
 Risks
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12. Killer App #1: Fatigue Detection
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 20% accidents fatigue-related1
 Heart: ECG captures Heart Rate
Variability
 Heart slowing is drowsiness warning
 QS body-based wearable sensor or belt-based
sensors (Plessy Semiconductor2)
 Breathing: Respiration sensor
measures cardiopulmonary activity
 Variation is early warning signal
 QS body-based wearable sensor or seat-cover
based sensors (Harken2)
 Posture Sensor (Lumo Back)
1Royal Society for the Prevention of Accidents. Driver Fatigue and Road Accidents: A Literature Review and
Position Paper. February 2001. http://www.rospa.com/roadsafety/info/fatigue.pdf
Also per Feng J and & Donmez B, Designing Feedback to Induce Sager Driving Behaviors, 2013.

13. Killer App #2: Real-time Parking/Assistance
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 Real-time Parking Assistance
 8-74% city congestion is looking for parking1
 Real-time parking services
 Vehicle navigation indicates available spots
 Automated reservation, payment, access
 Real-time Road-side Assistance
 Crash impact alert system
 Medical: Automatic first responder alert
 Service: Automatic vendor quote response
 Integration of multiple data streams
 Auto sensor data, QS data, smartcity data,
vendor network, community/group data
1Shoup, D., Cruising for Parking, Transport Policy, 2006, http://mashable.com/2011/04/13/smart-parking-tech/

14. Killer App #3: Anger/Stress-Reduction
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 QS wearable stress sensors linked to nav system
 Respiration, blood pressure, heart rate, GSR, glucose
 Smart steering wheel (Toyota ECG Steering Wheel1)
 Heart attack detection / warning,2 automatic daily health check
sent to personal blockchain EMR / QS data portal
 Intervention: breathing exercises, music, seat vibration
 Stress reduction “Leave on time” app
 Distracted driving active safety key stressor is being late
 Smarthome alert (calendar, traffic), drive stress tracking (time,
speed, braking, GPS), smartcity data (traffic, routing, parking)
 Incentive: linked health and auto insurance discounts
1http://www.psfk.com/2011/08/toyotas-ecg-steering-wheel-monitors-your-heart-rate-as-you-drive.html#!bOj0Qx
2US DOT, The Contribution of Medical Conditions to Crashes, 2009, http://www-nrd.nhtsa.dot.gov/Pubs/811219.pdf

15. Killer App #4: Keyless Authentication
 Target markets
 Auto rental
 Additional access
mode for personal
autos
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 Improve safe controlled auto access
 Digital authentication access system
 Smartphone/smartwatch Bluetooth app
 One-time/short-term automated access
 Tiered security levels (blockchain, fingerprint
reading, QR code, real-time confirmation)
 Auto tracking and location-based services
Example: Getaround Connect
Source: Swan et. al.; Smartphone-based Identity Authentication Platform; presented at Launch
Hackathon; San Francisco CA March 2013; special acknowledgement to Mark Adams.

16. Killer App #5: DIY Diagnostics
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 Automatic vehicle diagnostics app
 On-board diagnostics (OBD) port/Bluetooth to phone/watch/cloud
 Consumer OBD access (like DIYscience, DIYhealth)
 Mobile app for tracking and alerts, graphical scorecard
 Link to dealer/mechanic CRM and service quotes
 Pro-active cost-saving: improved consumer action-taking
for preventive maintenance and vehicle safety

17. Agenda
 Introduction to Quantified Self
 Automotive-QS Sensor Top 5 Killer Apps
 Fatigue Detection
 Real-time Parking and Assistance
 Anger/Stress Reduction
 Keyless Authentication
 DIY Diagnostics
 Conclusion
 Risks
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18. Auto-QS Sensors Conclusion
 Automotive Industry: numerous sweeping
and interlinked changes ahead
 Denser cities (5 bn in 2030), increased # cars
 Self-driving cars (20301)
 Fungible personal transport
 Uber, Lyft, Getaround, RelayRides
 Electric vehicles, biofuel, alternative fuel
 Suggests…
 New classes of transportation solutions required
 Interactive ‘Kirobo’ auto, not ‘dumb conveyance’
 Rethinking privacy and personal data rights
 Blockchain technology to allocate, validate,
access, and track transportation resources
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Kirobo Robot
Driver?
1“Emerging Technologies: Autonomous Cars—Not If, But When,” IHS Automotive, 2014,
http://press.ihs.com/press-release/automotive/self-driving-cars-moving-industrys-drivers-seat

19. September 4, 2014
Ann Arbor, MI USA
Slides: http://slideshare.net/LaBlogga
Melanie Swan
m@melanieswan.com
Sensor Ubiquity:
Blockchain Tech & Automotive-Quantified Self
Integrated Sensor Applications
Image credit: M. Ghezel