Big Data, Small Data

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The future of sensing in sustainability and health

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Big Data, Small Data

  1. 1. big data, small data: the future of sensing in health and sustainability eric c. larson | eclarson.com Assistant Professor Computer Science and Engineering
  2. 2. easy to get
  3. 3. hard to get easy to get
  4. 4. application hard to get easy to get
  5. 5. application application hard to get easy to get
  6. 6. data
  7. 7. data = knowledge
  8. 8. data you
  9. 9. personalization data you
  10. 10. personalization data you feedback
  11. 11. health and sustainability
  12. 12. small data in health and sustainability
  13. 13. small data in health and sustainability sensing you
  14. 14. small data in health and sustainability sensing sensing you stakeholders you
  15. 15. sustainability and water
  16. 16. sustainability and water lake mead 1983
  17. 17. lake mead 2011
  18. 18. lake mead 2011
  19. 19. $2,994.83
  20. 20. eco-feedback Geographic Comparisons Dashboards Metaphorical Unit Designs Recommendations
  21. 21. eco-feedback Geographic Comparisons Dashboards Metaphorical Unit Designs Recommendations
  22. 22. types of eco-feedback Annual % Savings 20% 20% 15% 12% 10% 6.8% 5% 8.4% 9.2% 3.8% 0% Enhanced 1 Billing Web 2 Based Daily 3 Feedback Realtime 4 Feedback Appliance Appliance Level 5 Untitled 1 + Personalized Level Feedback Based on 36 studies between 1995-2010. Summarized by Ehrhardt-Matinez et al.
 >20% reduction: Gardner et al. (2008) and Laitner et al. (2009)
  23. 23. types of eco-feedback Annual % Savings 20% 20% 15% aggregate 12% 10% 6.8% 5% 8.4% 9.2% 3.8% 0% Enhanced 1 Billing Web 2 Based Daily 3 Feedback Realtime 4 Feedback Appliance Appliance Level 5 Untitled 1 + Personalized Level Feedback Based on 36 studies between 1995-2010. Summarized by Ehrhardt-Matinez et al.
 >20% reduction: Gardner et al. (2008) and Laitner et al. (2009)
  24. 24. types of eco-feedback Annual % Savings 20% disaggregated 20% 15% aggregate 12% 10% 6.8% 5% 8.4% 9.2% 3.8% 0% Enhanced 1 Billing Web 2 Based Daily 3 Feedback Realtime 4 Feedback Appliance Appliance Level 5 Untitled 1 + Personalized Level Feedback Based on 36 studies between 1995-2010. Summarized by Ehrhardt-Matinez et al.
 >20% reduction: Gardner et al. (2008) and Laitner et al. (2009)
  25. 25. on average, a consumer can save 15-25%
  26. 26. bathroom usage
  27. 27. leaky flapper valve bathroom usage
  28. 28. mobile health
  29. 29. m health
  30. 30. iPhone 9 M Now with iColon. > slide to unlock
  31. 31. the promise of mhealth:
  32. 32. the promise of mhealth: revolutionize medicine
  33. 33. the promise of mhealth: revolutionize medicine eliminate doctor visits
  34. 34. the promise of mhealth: revolutionize medicine eliminate doctor visits remote / automatic diagnosis
  35. 35. the promise of mhealth: revolutionize medicine eliminate doctor visits remote / automatic diagnosis equalize developing countries
  36. 36. the promise of mhealth: revolutionize medicine eliminate doctor visits remote / automatic diagnosis equalize developing countries
  37. 37. the promise of mhealth: revolutionize medicine eliminate doctor visits remote / automatic diagnosis equalize developing countries
  38. 38. heart rate zombie run fitness trainer stress check current mhealth glucose buddy
  39. 39. heart rate stress check zombie run current mhealth 43,000 apps for health on the app store fitness trainer glucose buddy
  40. 40. heart rate stress check zombie run current mhealth 43,000 apps for health on the app store 96% are for calorie counting & exercise fitness trainer glucose buddy
  41. 41. heart rate stress check zombie run current mhealth 43,000 apps for health on the app store 96% are for calorie counting & exercise 4% are remote monitoring fitness trainer glucose buddy
  42. 42. heart rate stress check zombie run current mhealth 43,000 apps for health on the app store 96% are for calorie counting & exercise 4% are remote monitoring ~100 are cleared by the FDA fitness trainer glucose buddy
  43. 43. consider physician’s needs
  44. 44. consider physician’s needs connecting with patient
  45. 45. consider physician’s needs connecting with patient tracking baselines
  46. 46. consider physician’s needs connecting with patient tracking baselines personalized trending data
  47. 47. consider physician’s needs connecting with patient tracking baselines personalized trending data managing chronic disease
  48. 48. consider physician’s needs connecting with patient tracking baselines personalized trending data managing chronic disease 30% of all US healthcare spending is on chronic disease
  49. 49. mhealth and chronic disease management
  50. 50. mhealth and chronic disease management compliance? cost? doctor patient? data reliability?
  51. 51. compliance
  52. 52. compliance
  53. 53. baseline sensor quantity
  54. 54. phone as a sensor baseline sensor quantity
  55. 55. phone as a sensor baseline embedded sensors sensor quantity
  56. 56. phone as a sensor baseline embedded sensors sensor estimated quantity processing
  57. 57. accelerometer gyroscope magnetometer /compass dual camera / flash 1+ microphones proximity sensor capacitive sensor gps motorized actuator wireless antenna (s)
  58. 58. compliance++; cost--; dr_pat *= 10; accelerometer gyroscope magnetometer /compass dual camera / flash 1+ microphones proximity sensor capacitive sensor gps motorized actuator wireless antenna (s)
  59. 59. accelerometer gyroscope magnetometer /compass dual camera / flash 1+ microphones compliance++; proximity sensor cost--; capacitive sensor dr_pat *= 10; gps motorized actuator data reliability? wireless antenna (s)
  60. 60. what can the mobile phone sense with clinical accuracy?
  61. 61. what can the mobile phone sense with clinical accuracy? which chronic diseases should be targeted first?
  62. 62. volume flow spirometry testing time volume
  63. 63. volume flow spirometry testing time volume
  64. 64. volume flow spirometry testing time volume
  65. 65. spirometry
  66. 66. spirometry standard of care for:
  67. 67. spirometry standard of care for: asthma COPD post-transplant breathing cystic fibrosis
  68. 68. home spirometry
  69. 69. home spirometry ! faster detection rapid recovery trending
  70. 70. challenges with home spirometry high cost barrier patient compliance less coaching limited integration
  71. 71. flow rate volume airflow sensor lung function
  72. 72. flow rate volume airflow sensor sound pressure microphone lung function
  73. 73. flow rate volume airflow sensor estimated lung function sound pressure microphone processing
  74. 74. SpiroSmart availability cost portability more effective coaching interface integrated uploading
  75. 75. Using SpiroSmart
  76. 76. SpiroSmart
  77. 77. SpiroSmart
  78. 78. within ~5-7% of a real spirometer phone app doctor visit
  79. 79. appropriate for trending and screening
  80. 80. global health non-profit
  81. 81. global health non-profit patient and doctors
  82. 82. global health non-profit patient and doctors pharmaceutical drug trials
  83. 83. currently undergoing FDA 510k device approval diagnostic deployment in India started in January
  84. 84. other applications in health? mhealth and newborns?
  85. 85. neonatal jaundice in the US kernicterus: 21 hazardous jaundice: 1158 extreme jaundice: 2,317 severe jaundice: 35,000 phototherapy: 290,000 visible jaundice: 3.5 million births/year: 4.1 million
  86. 86. Method Accuracy Disadvantages TSB Gold standard (r=1.0) Painful, costly, inconvenient, delayed Accurate (r= 0.75 -0.93) Meter = $7000 tips $5 unavailable in most physician offices TcB Visual assessment (provider or parent) not accurate (r= 0.36 - 0.7), underestimates severity No standardization lighting, pigmentation
  87. 87. bilirubin level in blood blood draw jaundice level
  88. 88. bilirubin level in blood blood draw yellowness camera jaundice level
  89. 89. bilirubin level in blood blood draw estimated jaundice level yellowness camera processing
  90. 90. bilicam
  91. 91. bilicam correlates with TSB at 0.84 TcB correlates anywhere from 0.71 to 0.9
  92. 92. mobile apps are poised to take over the market nokia x-challenge: grand prize: mobile device that monitors vitals signs can diagnose 10 of 15 diseases
  93. 93. the future of mHealth?
  94. 94. the future of mHealth? • new sensors will be integrated into phones
  95. 95. the future of mHealth? • new sensors will be integrated into phones • health sensing and mobile phone manufacturing will become highly intertwined
  96. 96. the future of mHealth? • new sensors will be integrated into phones • health sensing and mobile phone manufacturing will become highly intertwined • FDA will have a big say in how quickly phones can be built and pushed out, and what apps are “OK”
  97. 97. the future of mHealth? • new sensors will be integrated into phones • health sensing and mobile phone manufacturing will become highly intertwined • FDA will have a big say in how quickly phones can be built and pushed out, and what apps are “OK”
  98. 98. the future of mHealth? • new sensors will be integrated into phones • health sensing and mobile phone manufacturing will become highly intertwined • FDA will have a big say in how quickly phones can be built and pushed out, and what apps are “OK” • FDA sanctioned validity study
  99. 99. the future of mHealth? • new sensors will be integrated into phones • health sensing and mobile phone manufacturing will become highly intertwined • FDA will have a big say in how quickly phones can be built and pushed out, and what apps are “OK” • FDA sanctioned validity study • four melanoma apps
  100. 100. the future of mHealth? • new sensors will be integrated into phones • health sensing and mobile phone manufacturing will become highly intertwined • FDA will have a big say in how quickly phones can be built and pushed out, and what apps are “OK” • FDA sanctioned validity study • four melanoma apps • 30% of cancerous moles classified as benign
  101. 101. what app makes a phone a medical device?
  102. 102. what app makes a phone a medical device? apps that transform a phone or connect to a medical sensor
  103. 103. what app makes a phone a medical device? apps that transform a phone or connect to a medical sensor apps that control a device through a phone
  104. 104. what app makes a phone a medical device? apps that transform a phone or connect to a medical sensor apps that control a device through a phone apps that display, transfer, store, medical device data
  105. 105. what app makes a phone a medical device? apps that transform a phone or connect to a medical sensor apps that control a device through a phone apps that display, transfer, store, medical device data apps that interact with electronic health records or simple health data
  106. 106. m ay be ye s ye s ye s what app makes a phone a medical device? apps that transform a phone or connect to a medical sensor apps that control a device through a phone apps that display, transfer, store, medical device data apps that interact with electronic health records or simple health data
  107. 107. m ay be ye s ye s ye s what app makes a phone a medical device? apps that transform a phone or connect to a medical sensor apps that control a device through a phone apps that display, transfer, store, medical device data apps that interact with electronic health records or simple health data psychiatry, medical dosing, symptom logs, general fitness apps…
  108. 108. m ay be m ay be ye s ye s ye s what app makes a phone a medical device? apps that transform a phone or connect to a medical sensor apps that control a device through a phone apps that display, transfer, store, medical device data apps that interact with electronic health records or simple health data psychiatry, medical dosing, symptom logs, general fitness apps…
  109. 109. what app makes a phone a medical device? we will see?
  110. 110. some approved apps
  111. 111. some approved apps
  112. 112. some approved apps
  113. 113. some approved apps
  114. 114. some approved apps
  115. 115. my take
  116. 116. eclarson.com eclarson@lyle.smu.edu > slide to unlock @ec_larson Thank You!
  117. 117. big data, small data: the future of sensing in health and sustainability eclarson.com eclarson@lyle.smu.edu > slide to unlock @ec_larson collaborators: ! Joseph Camp Shwetak Patel Jim Stout, MD Jim Taylor, MD Margaret Rosenfeld, MD Gaetano Boriello Mayank Goel Lilian DeGreef Thank You! eric c. larson | eclarson.com Assistant Professor Computer Science and Engineering
  118. 118. big data, small data: the future of sensing in health and sustainability eclarson.com eclarson@lyle.smu.edu @ec_larson collaborators: ! Joseph Camp Shwetak Patel Jim Stout, MD Jim Taylor, MD Margaret Rosenfeld, MD Gaetano Boriello Mayank Goel Lilian DeGreef Thank You! eric c. larson | eclarson.com Assistant Professor Computer Science and Engineering

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