Ubicomp2012 spiro smartpresentation

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Ubicomp2012 spiro smartpresentation

  1. 1. Using a Microphone to Measure Lung Function on a Mobile Phone SpiroSmart University of Washington Eric Larson, Mayank Goel, Gaetano Borriello, Sonya Heltshe, Margaret Rosenfeld, Shwetak Patel UbiComp Lab Friday, September 7, 12
  2. 2. What is SpiroSmart? A smartphone based spirometer that leverages on-device microphone to help you keep track of your lung function. Friday, September 7, 12
  3. 3. Performance As good as a home spirometer Error within 5.1% in a study with 52 participants Friday, September 7, 12
  4. 4. Performance As good as a home spirometer Error within 5.1% in a study with 52 participants > $2000 Friday, September 7, 12
  5. 5. What is SpiroSmart? A smartphone based spirometer that helps you keep track of your lung function. Friday, September 7, 12
  6. 6. What is SpiroSmart? A smartphone based spirometer that helps you keep track of your lung function.lung function spirometer Friday, September 7, 12
  7. 7. Spirometer? Device that measures amount of air inhaled and exhaled. Friday, September 7, 12
  8. 8. Lung Function? For example: Asthma COPD Cystic Fibrosis Chronic Bronchitis Evaluates severity of pulmonary impairments. Friday, September 7, 12
  9. 9. Using a spirometer Flow Volume Volume Time Friday, September 7, 12
  10. 10. Using a spirometer Flow Volume Volume Time Friday, September 7, 12
  11. 11. Using a spirometer Flow Volume Volume Time Friday, September 7, 12
  12. 12. Volume-Time Graph Volume Time Friday, September 7, 12
  13. 13. Volume-Time Graph Volume Time Friday, September 7, 12
  14. 14. Volume-Time Graph Volume Time FEV1 FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity Friday, September 7, 12
  15. 15. Volume-Time Graph Volume Time1 sec. FEV1 FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity Friday, September 7, 12
  16. 16. Volume-Time Graph Volume Time1 sec. FEV1 FVC FEV1% = FEV1/FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity Friday, September 7, 12
  17. 17. Volume-Time Graph FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity FEV1% = FEV1/FVC Friday, September 7, 12
  18. 18. Volume-Time Graph FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity FEV1% = FEV1/FVC > 80% Healthy 60 - 79% Mild 40 - 59% Moderate < 40% Severe Friday, September 7, 12
  19. 19. Flow-Volume Graph Flow Volume Friday, September 7, 12
  20. 20. Flow-Volume Graph Flow Volume Friday, September 7, 12
  21. 21. Flow-Volume Graph Flow Volume FEV1 FVC PEF PEF: Peak Expiratory Flow FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity Friday, September 7, 12
  22. 22. Flow-Volume Graph Flow Volume FEV1 FVC 1 sec. PEF PEF: Peak Expiratory Flow FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity Friday, September 7, 12
  23. 23. Flow-Volume Graph Flow Volume Normal Friday, September 7, 12
  24. 24. Flow-Volume Graph Flow Volume Normal Obstructive Friday, September 7, 12
  25. 25. Obstructive Diseases Resistance in air path leads to reduced air flow Friday, September 7, 12
  26. 26. Obstructive Diseases Resistance in air path leads to reduced air flow Friday, September 7, 12
  27. 27. Restrictive Diseases Lungs are unable to pump enough air and pressure Friday, September 7, 12
  28. 28. Restrictive Diseases Lungs are unable to pump enough air and pressure Friday, September 7, 12
  29. 29. Flow-Volume Graph Flow Volume Normal Obstructive Friday, September 7, 12
  30. 30. Flow-Volume Graph Flow Volume Normal Restrictive Obstructive Friday, September 7, 12
  31. 31. Clinical Spirometery Friday, September 7, 12
  32. 32. Home Spirometery Friday, September 7, 12
  33. 33. Home Spirometery Faster detection Rapid recovery Friday, September 7, 12
  34. 34. Home Spirometery High cost barrier Patient compliance Less coaching Limited feedback Challenges with Friday, September 7, 12
  35. 35. SpiroSmart Availability Cost Portability More effective coaching interface Integrated uploading Friday, September 7, 12
  36. 36. Using SpiroSmart Friday, September 7, 12
  37. 37. Using SpiroSmart Friday, September 7, 12
  38. 38. Using SpiroSmart ] Friday, September 7, 12
  39. 39. Using SpiroSmart ] Friday, September 7, 12
  40. 40. Using SpiroSmart ] Friday, September 7, 12
  41. 41. Using SpiroSmart Friday, September 7, 12
  42. 42. Initial Study Design x 3 x 3 Friday, September 7, 12
  43. 43. Initial Study Design x 3 x 3 Friday, September 7, 12
  44. 44. Initial Study Design x 3 x 3 Friday, September 7, 12
  45. 45. Need of attachments Friday, September 7, 12
  46. 46. Need of attachments Friday, September 7, 12
  47. 47. Mouthpiece Friday, September 7, 12
  48. 48. Sling Friday, September 7, 12
  49. 49. Study Design x 3 x 3 Friday, September 7, 12
  50. 50. + Study Design x 3 x 3 x 3 + + x 3 x 3 Friday, September 7, 12
  51. 51. + Study Design x 3 x 3 x 3 + + x 3 x 3 Friday, September 7, 12
  52. 52. + Study Design x 3 x 3 x 3 + + x 3 x 3 Friday, September 7, 12
  53. 53. + Study Design x 3 x 3 x 3 + + x 3 x 3 Friday, September 7, 12
  54. 54. + Study Design x 3 x 3 x 3 + + x 3 x 3 Friday, September 7, 12
  55. 55. dataset + + + Friday, September 7, 12
  56. 56. subjects 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + + Friday, September 7, 12
  57. 57. subjects 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + + Friday, September 7, 12
  58. 58. subjects 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + + Friday, September 7, 12
  59. 59. subjects 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + + Friday, September 7, 12
  60. 60. subjects 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + + Friday, September 7, 12
  61. 61. dataset Friday, September 7, 12
  62. 62. dataset flow features Friday, September 7, 12
  63. 63. dataset flow features measures regression Friday, September 7, 12
  64. 64. dataset flow features measures regression FEV1 FVC PEF Friday, September 7, 12
  65. 65. dataset flow features measures regression curve regression FEV1 FVC PEF Friday, September 7, 12
  66. 66. 0 1 2 3 4 0 5 10 15 Flow(L/s) Volume(L) 1 2 3 4 Volume(L) 0 1 2 3 4 0 5 10 15 Flow(L/s) Volume(L) 0 2 4 6 8 10 0 1 2 3 4 time(s) Volume(L) dataset flow features measures regression curve regression FEV1 FVC PEF Friday, September 7, 12
  67. 67. 0 1 2 3 4 0 5 10 15 Flow(L/s) Volume(L) 1 2 3 4 Volume(L) 0 1 2 3 4 0 5 10 15 Flow(L/s) Volume(L) 0 2 4 6 8 10 0 1 2 3 4 time(s) Volume(L) dataset flow features measures regression curve regression lung function FEV1 FVC PEF Friday, September 7, 12
  68. 68. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features Friday, September 7, 12
  69. 69. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection Friday, September 7, 12
  70. 70. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection Friday, September 7, 12
  71. 71. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection Friday, September 7, 12
  72. 72. time(s) frequency(Hz) 1 2 3 4 5 6 0 500 1000 1500 2000 2500 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection Friday, September 7, 12
  73. 73. time(s) frequency(Hz) 1 2 3 4 5 6 0 500 1000 1500 2000 2500 time(s) frequency(Hz) 1 2 3 4 5 6 0 500 1000 1500 2000 2500 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection Friday, September 7, 12
  74. 74. time(s) frequency(Hz) 1 2 3 4 5 6 0 500 1000 1500 2000 2500 time(s) frequency(Hz) 1 2 3 4 5 6 0 500 1000 1500 2000 2500 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking Friday, September 7, 12
  75. 75. time(s) frequency(Hz) 1 2 3 4 5 6 0 500 1000 1500 2000 2500 time(s) frequency(Hz) 1 2 3 4 5 6 0 500 1000 1500 2000 2500 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking Friday, September 7, 12
  76. 76. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking Friday, September 7, 12
  77. 77. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking filtersource output Friday, September 7, 12
  78. 78. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking filtersource output estimated Friday, September 7, 12
  79. 79. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 lpc8raw time(s) amplitude auto-regressive estimate filtersource output estimated Friday, September 7, 12
  80. 80. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude envelope detection 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude resonance tracking 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 lpc8raw time(s) amplitude auto-regressive estimate filtersource output estimated Friday, September 7, 12
  81. 81. flow features −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) Friday, September 7, 12
  82. 82. flow features −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) ground truth spirometer Friday, September 7, 12
  83. 83. flow features 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) ground truth spirometer feature 1 Friday, September 7, 12
  84. 84. flow features −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) ground truth spirometer feature 1 feature 2 Friday, September 7, 12
  85. 85. flow features −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) ground truth spirometer −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue feature 1 feature 2 Friday, September 7, 12
  86. 86. lung function dataset flow features measures regression curve regression Friday, September 7, 12
  87. 87. −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue measures regressionground truth feature 1 feature 2 Friday, September 7, 12
  88. 88. −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) measures regression −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue ground truth feature 1 feature 2 Friday, September 7, 12
  89. 89. −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) measures regression −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 7.1 0.33 0.35 PEF featuresground truth feature 1 feature 2 Friday, September 7, 12
  90. 90. −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) measures regression −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 7.1 0.33 0.35 3.2 0.12 0.17 FEV1 features PEF featuresground truth feature 1 feature 2 Friday, September 7, 12
  91. 91. measures regression FEV1 features PEF features Friday, September 7, 12
  92. 92. measures regression FEV1 features bagged decision tree PEF features bagged decision tree Friday, September 7, 12
  93. 93. measures regression FEV1 features bagged decision tree output PEF features bagged decision tree output Friday, September 7, 12
  94. 94. lung function dataset flow features measures regression curve regression Friday, September 7, 12
  95. 95. resultslung function measures regression Mean%Error Lower is better Friday, September 7, 12
  96. 96. resultslung function measures regression FEV1 FVC FEV1% PEF 0 1 2 3 4 5 6 7 Mean%Error Lower is better Friday, September 7, 12
  97. 97. resultslung function measures regression FEV1 FVC FEV1% PEF 0 1 2 3 4 5 6 7 Mean%Error Lower is better Friday, September 7, 12
  98. 98. resultslung function measures regression FEV1 FVC FEV1% PEF 0 1 2 3 4 5 6 7 No Personalization Personalization Mean%Error Lower is better Friday, September 7, 12
  99. 99. resultslung function measures regression Average Error = 5.1% ATS Criteria = 5-7% Friday, September 7, 12
  100. 100. resultslung function measures regression Average Error = 5.1% ATS Criteria = 5-7% normal Attachments have no effect on Error Friday, September 7, 12
  101. 101. lung function dataset flow features measures regression curve regression Friday, September 7, 12
  102. 102. curve regression −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue feature 1 feature 2 Friday, September 7, 12
  103. 103. −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) curve regression −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue feature 1 feature 2 curve output Friday, September 7, 12
  104. 104. curve regression bagged decision tree −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue Friday, September 7, 12
  105. 105. curve regression bagged decision tree −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue CRF Friday, September 7, 12
  106. 106. curve regression bagged decision tree −1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 time(s) featurevalue CRF −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) Friday, September 7, 12
  107. 107. example curvescurve regression −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) Friday, September 7, 12
  108. 108. example curvescurve regression −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) Friday, September 7, 12
  109. 109. example curvescurve regression −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) Friday, September 7, 12
  110. 110. example curvescurve regression −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −2 0 2 4 6 0 5 10 15 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 volume(L) flow(L/s) Friday, September 7, 12
  111. 111. lung function dataset flow features measures regression curve regression Friday, September 7, 12
  112. 112. evaluationcurve regression lung function Friday, September 7, 12
  113. 113. evaluationcurve regression 10 subjects curves lung function Friday, September 7, 12
  114. 114. evaluationcurve regression 5 pulmonologists 10 subjects curves lung function Friday, September 7, 12
  115. 115. evaluationcurve regression 5 pulmonologists 10 subjects curves unaware if from SpiroSmart / Spirometer lung function Friday, September 7, 12
  116. 116. surveycurve regression lung function Friday, September 7, 12
  117. 117. surveycurve regression lung function Friday, September 7, 12
  118. 118. surveycurve regression lung function Friday, September 7, 12
  119. 119. surveycurve regression lung function Friday, September 7, 12
  120. 120. surveycurve regression lung function Friday, September 7, 12
  121. 121. results curve regression normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate lung function identical 32 / 50 Friday, September 7, 12
  122. 122. results curve regression normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate lung function identical one off 32 / 50 5 / 18 Friday, September 7, 12
  123. 123. results curve regression normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate lung function identical one off 32 / 50 5 / 18 Friday, September 7, 12
  124. 124. results curve regression normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate lung function identical one off 32 / 50 5 / 18 Friday, September 7, 12
  125. 125. results curve regression normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate lung function identical one off 32 / 50 5 / 18 Friday, September 7, 12
  126. 126. limitations Friday, September 7, 12
  127. 127. limitations no inhalation quiet surroundings Friday, September 7, 12
  128. 128. limitations no inhalation quiet surroundings coaching Friday, September 7, 12
  129. 129. limitations no inhalation quiet surroundings coaching smartphone Friday, September 7, 12
  130. 130. limitations no inhalation quiet surroundings coaching smartphone trends over time Friday, September 7, 12
  131. 131. current work no inhalation quiet surroundings coaching smartphone trends over time Friday, September 7, 12
  132. 132. current work asthma study underway no inhalation quiet surroundings coaching smartphone trends over time Friday, September 7, 12
  133. 133. current work asthma study underway incentive graphicsno inhalation quiet surroundings coaching smartphone trends over time Friday, September 7, 12
  134. 134. current work asthma study underway incentive graphics air quality, non-chronic disease no inhalation quiet surroundings coaching smartphone trends over time Friday, September 7, 12
  135. 135. current work asthma study underway incentive graphics air quality, non-chronic disease call in service no inhalation quiet surroundings coaching smartphone trends over time Friday, September 7, 12
  136. 136. call in service Friday, September 7, 12
  137. 137. Using a Microphone to Measure Lung Function on a Mobile Phone SpiroSmart eclarson.com eclarson@uw.edu mayankgoel.com mayankg@uw.edu University of Washington Eric Larson, Mayank Goel, Gaetano Borriello, Sonya Heltshe, Margaret Rosenfeld, Shwetak Patel UbiComp Lab Friday, September 7, 12
  138. 138. backup - curves quantitative Friday, September 7, 12
  139. 139. backup - bland altman igure 6. Cumulative error plot of the percentage of the results (y-axis) within the percent error of the x-axis, shown for “normal,” abnormal” and “personalized abnormal” groups. Also shown are the accuracies for each measure, defined as the percentage of measures within accepted clinical limits (i.e., the variation one would expect on a traditional spirometer). ature is taken. For FEV1, the integration of the features uring the first second is used (Figure 5). egression is implemented using bagged decision trees and ean square error; 100 trees are used in each forest. Each aining subset is used to predict lung function for a given st instance, resulting in an ensemble of predictions. The nal decision is made by clustering the ensemble using k- eans (k=2). The centroid of the cluster with the most in- ances is the final prediction of PEF, FEV1, or FVC. urve Shape Regression: The shape of the curve is a more fficult and involved regression. Instead of a single meas- roSmart and a clinical spirometer. Based on our evaluation we conclude that SpiroSmart will meet the needs of home lung function monitoring. Estimate of Lung Function Measures We breakdown the comparison of measurements from Spi roSmart and a clinical spirometer by how the percent erro is distributed and how well this conforms to accepted clini cal variances in each measure. Distribution of Percent Error in Lung Function Measures The curves in Figure 6 present the cumulative percentage error plots for FVC, FEV1, PEF, and FEV1/FVC. Explana e 7. Bland Altman plots of percent error between SpiroSmart and a clinical spirometer versus the value obtained from th al spirometer. th th percentiles (short dashes) are also shown. Outliers in each plot are tFriday, September 7, 12
  140. 140. backup - mouthpiece sling Figure 8. Average percent error for each configuration. level of agreement and reproducibility between spirometry measures is highly correlated with how experienced the subject is at performing the tests [22]. The only other varia- ble suggestive of being associated with bias magnitude (p<0.1) was mouthpiece use. Friday, September 7, 12
  141. 141. backup - survey table Curve Range of Diagnoses Within Rater Spirometer SpiroSmart Agree One-off 1 Norm-Obs. (Mild) Norm-Obs. (Min.) 3 1 2 Norm-Obs. (Mild) Norm-Obs. (Mild) 3 1 3 Normal Norm-Obs. (Mild) 3 1 4 Normal Normal 5 0 5 Normal Normal 5 0 6 Obs. (Min.-Mod.) Restrictive 0 0 7 Obs. (Mild-Mod.) Norm-Obs. (Mild) 0 2 8 Insuff.-Norm Insuff.-Norm 3 0 9 Normal Normal 5 0 Normal Normal 5 0 Total N/A N/A 32/50 5/18 Table 2. Summary of survey responses from 5 pulmonologists. Also shown are the number of times a pulmonologist’s diagno- Fig- ction ions. sig- nifi- piece For error This re of on— Im- ixed udiesFriday, September 7, 12
  142. 142. backup - features after gone through phone Friday, September 7, 12
  143. 143. backup - • what else? • Friday, September 7, 12

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