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Nuria Oliver, PhD Researcher Microsoft Research. 2 New opportunities for wearable health and wellness continuous monitoring devices.

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Presentation on theme: "Nuria Oliver, PhD Researcher Microsoft Research. 2 New opportunities for wearable health and wellness continuous monitoring devices."— Presentation transcript:

1 Nuria Oliver, PhD Researcher Microsoft Research

2 2 New opportunities for wearable health and wellness continuous monitoring devices

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4 Zillions of gadgets to monitor various physiological and environmental signals Typically proprietary data transmission protocols Typically capture on-board and off-line processing of the data after downloading to a PC Very difficult if not impossible to combine signals from different gadgets Lack of real-time on-board processing of the information to deliver value to the user

5 Continuous monitoring and understanding of multiple physiological and environmental signals Finding correlations between lifestyle and wellness/health Identifying trends and deviations from those trends Developing new interesting and fun applications Empowering users 5

6 6 Audiovox 5600 GSM mobile phone Alivetec Alive ECG and Accelerometer sensor Blood oximeter HealthGear MPTrain Automatically Monitor and Detect Sleep Apnea Events Music and Physiology-based Personal Trainer

7 Real-time, wearable physiological monitoring system on a mobile phone Physiological sensors wirelessly connected via Bluetooth to a mobile phone Continuous recording of blood oximetry, heart-rate and plethysmographic signal Real-time analysis and presentation of physiological data to the user Usage scenario: Sleep monitoring and automatic sleep apnea detection 7

8 Underdiagnosed but common condition Affects children and adults 4% in men and 2% in women (higher for elderly) Untreated causes $3.4 billion of medical costs 40 million undiagnosed Americans Periods of interrupted breathing (apnea) and periods of reduced breathing (hypoapnea) Leads to Hypoxia, asphyxia and awakenings Increased heart-rate, high blood pressure Extreme fatigue, poor concentration Compromised immune system Cardio/cerebrovascular problems 8

9 Nocturnal Polysomnography (PSG) In sleep center for 1-2 nights Continuous, simultaneous multi-channel measurements of 8 physiological signals Very expensive, cumbersome, time consuming, just one sample and subject to manual scoring and human error Pulse oximetry Useful as screening tool One simple, light-weight sensor on finger, toe or earlobe 9

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11 20 volunteers 80% male, ages % healthy, 70% with sleep apnea or suspected Filled out pre-sleep and post-sleep questionnaires Wore HealthGear for one or two full nights in their own homes 11

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15 3 long-term (2-3 weeks at a time) sleep studies 1 long-term (2.5 months) study with MITs PlaceLab Investigated correlations between quality of sleep and lifestyle factors Alcohol and caffeine intake Diet Exercise Stress levels 15

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17 New hardware board!! More long-term sleep studies (MIT, Univ. Virginia) Study on drivers that have apnea (Univ. Iowa) Study on aircraft pilots Summits of Canada expedition

18 18 Audiovox 5600 GSM mobile phone Alivetec Alive ECG and Accelerometer sensor HealthGear MPTrain Automatically Monitor and Detect Sleep Apnea Events Music and Physiology-based Personal Trainer

19 Music Provides Pacing Advantage Distraction Positive Mood Music Positively Impacts Endurance Performance Perception Exertion Perception 19

20 Target HR zones recovery, weight management, aerobic, anaerobic, over-exertion Used to measure effort 20 PHR reserve =(maxHR - restHR)*P + restHR

21 Next Action Module Next Action Module BPM(t) = Current Heart rate SPM(t) = Current pace BPM(t) = Current Heart rate SPM(t) = Current pace DBP M(t) >0 <0 Slow down user Keep same speed Speed up user DBPM(t) = BPM(t) – Ideal BPM(t) DBPM(t) = BPM(t) – Ideal BPM(t)

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24 Runner study with 18 runners to test a few new features of the system 24

25 Real-time collection and analysis of physiological and environmental signals on a mobile phone (Smartphone) Big opportunity for Constant monitoring of users Real-time feedback to users Finding correlations between lifestyle and health/wellness Identifying trends and deviations from those trends New applications and services 25

26 High impact on the society Improving Quality of Life of Users Aging, chronic disease management, exercise, wellness, entertainment, affective and social computing, disease prevention, pro-active medicine, stress reduction, etc.. 26

27 More information

28 © 2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

29 Microsoft Research Faculty Summit 2007


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