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- 1. enabling eco-feedback and out-of-clinic health sensing indirect University of Washington Eric C. Larson UbiComp Lab electrical engineering computer science and engineering ubiquitous sensing
- 13. indirect sensing •not exact •calibration •low cost •easier to deploy
- 14. indirect sensing •not exact •calibration •low cost •easier to deploy •readily accepted
- 15. digital signal processing digital signal processing machine learning machine learning HCI mobile phone embedded references image processing time series evol. comp. ensemble/ graphical HCI mobile phone embedded references thermal imaging CHI 2010 ESPA 2012 facial analysis IJAEC 2010 image fidelity ICIP 2009 JEI 2010 power harvesting UbiComp 2010 gas sensing Pervasive 2010 water sensing UbiComp 2009 Pervasive 2011 CHI 2012 cough sensing UbiComp 2011 lung function UbiComp 2012 DEV 2013 interaction& imageanalysis sustainabilityhealth
- 16. digital signal processing digital signal processing machine learning machine learning HCI mobile phone embedded references image processing time series evol. comp. ensemble/ graphical HCI mobile phone embedded references thermal imaging CHI 2010 ESPA 2012 facial analysis IJAEC 2010 image fidelity ICIP 2009 JEI 2010 power harvesting UbiComp 2010 gas sensing Pervasive 2010 water sensing UbiComp 2009 Pervasive 2011 CHI 2012 cough sensing UbiComp 2011 lung function UbiComp 2012 DEV 2013 interaction& imageanalysis sustainabilityhealth
- 17. digital signal processing digital signal processing machine learning machine learning HCI mobile phone embedded references image processing time series evol. comp. ensemble/ graphical HCI mobile phone embedded references thermal imaging CHI 2010 ESPA 2012 facial analysis IJAEC 2010 image fidelity ICIP 2009 JEI 2010 power harvesting UbiComp 2010 gas sensing Pervasive 2010 water sensing UbiComp 2009 Pervasive 2011 CHI 2012 cough sensing UbiComp 2011 lung function UbiComp 2012 DEV 2013 interaction& imageanalysis sustainabilityhealth
- 18. digital signal processing digital signal processing machine learning machine learning HCI mobile phone embedded references image processing time series evol. comp. ensemble/ graphical HCI mobile phone embedded references thermal imaging CHI 2010 ESPA 2012 facial analysis IJAEC 2010 image fidelity ICIP 2009 JEI 2010 power harvesting UbiComp 2010 gas sensing Pervasive 2010 water sensing UbiComp 2009 Pervasive 2011 CHI 2012 cough sensing UbiComp 2011 lung function UbiComp 2012 DEV 2013 interaction& imageanalysis sustainabilityhealth
- 19. digital signal processing digital signal processing machine learning machine learning HCI mobile phone embedded references image processing time series evol. comp. ensemble/ graphical HCI mobile phone embedded references thermal imaging CHI 2010 ESPA 2012 facial analysis IJAEC 2010 image fidelity ICIP 2009 JEI 2010 power harvesting UbiComp 2010 gas sensing Pervasive 2010 water sensing UbiComp 2009 Pervasive 2011 CHI 2012 cough sensing UbiComp 2011 lung function UbiComp 2012 DEV 2013 interaction& imageanalysis sustainabilityhealth
- 20. digital signal processing digital signal processing machine learning machine learning HCI mobile phone embedded references image processin g time series evol. comp. ensembl egraphic al HCI mobile phone embedded references thermal imaging CHI 2010 ESPA 2012 facial analysis IJAEC 2010 image fidelity ICIP 2009 JEI 2010 power harvesting UbiComp 2010 gas sensing Pervasive 2010 UbiComp 2009 Pervasive 2011 CHI 2012 cough sensing UbiComp 2011 UbiComp 2012 DEV 2013 interaction imageanalysis sustainabilityhealth water sensing lung function
- 24. how can indirect sensing and machine learning be used for sustainability?
- 25. we are using water faster than it is being replenished Pacific Institute for Studies in Development, Environment, and Security, 2011
- 26. we are using water faster than it is being replenished Pacific Institute for Studies in Development, Environment, and Security, 2011
- 29. $2,994.83
- 33. water usage is vastly misunderstood
- 34. eco-feedback Geographic Comparisons Dashboards Metaphorical Unit Designs Recommendations
- 35. eco-feedback Geographic Comparisons Dashboards Metaphorical Unit Designs Recommendations
- 37. 0% 5% 10% 15% 20% 1 2 3 4 5 Untitled 1 20% 12% 9.2%8.4% 6.8% 3.8% Enhanced Billing Web Based Daily Feedback Realtime Feedback Appliance Level + Personalized Feedback Annual%Savings Based on 36 studies between 1995-2010. Summarized by Ehrhardt-Matinez et al. >20% reduction: Gardner et al. (2008) and Laitner et al. (2009) Appliance Level eco-feedback in electricity
- 38. 0% 5% 10% 15% 20% 1 2 3 4 5 Untitled 1 20% 12% 9.2%8.4% 6.8% 3.8% Enhanced Billing Web Based Daily Feedback Realtime Feedback Appliance Level + Personalized Feedback Annual%Savings Based on 36 studies between 1995-2010. Summarized by Ehrhardt-Matinez et al. >20% reduction: Gardner et al. (2008) and Laitner et al. (2009) Appliance Level eco-feedback in electricity aggregate
- 39. 0% 5% 10% 15% 20% 1 2 3 4 5 Untitled 1 20% 12% 9.2%8.4% 6.8% 3.8% Enhanced Billing Web Based Daily Feedback Realtime Feedback Appliance Level + Personalized Feedback Annual%Savings Based on 36 studies between 1995-2010. Summarized by Ehrhardt-Matinez et al. >20% reduction: Gardner et al. (2008) and Laitner et al. (2009) Appliance Level eco-feedback in electricity aggregate disaggregated
- 45. meters flow rate fixture flow inline water
- 46. meters flow rate fixture flow inline water water pressure pressure sensor
- 47. meters flow rate fixture flow inline water water pressure pressure sensor
- 48. meters flow rate fixture flow inline water water pressure pressure sensor machine learning estimated
- 49. HydroSense
- 51. • single sensor • easy to install HydroSense
- 52. • single sensor • easy to install • low cost HydroSense
- 53. • single sensor • easy to install • low cost • can observe every fixture HydroSense
- 54. HydroSense
- 55. HydroSense
- 56. 40# 50# 60# 70# 80# Cold Line Pressure (Hose Spigot) 0 94.5 time (s) psi HydroSense
- 57. 40# 50# 60# 70# 80# Cold Line Pressure (Hose Spigot) 0 94.5 time (s) psi HydroSense
- 58. 40# 50# 60# 70# 80# Cold Line Pressure (Hose Spigot) 0 94.5 time (s) psi HydroSense open close
- 59. 40# 50# 60# 70# 80# Cold Line Pressure (Hose Spigot) 0 94.5 time (s) psi HydroSense open close kitchen sink
- 60. 40# 50# 60# 70# 80# Cold Line Pressure (Hose Spigot) 0 94.5 time (s) psi HydroSense open close kitchen sink upstairs toilet
- 61. 40# 50# 60# 70# 80# Cold Line Pressure (Hose Spigot) 0 94.5 time (s) psi HydroSense open close kitchen sink downstairs toilet upstairs toilet
- 62. kitchen sink upstairs toilet downstairs toilet template matching unknown event
- 63. kitchen sink upstairs toilet downstairs toilet template matching unknown event
- 64. initial study Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244. Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged experiments. Pervasive and Mobile Computing, (2010).
- 65. initial study • 10 homes Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244. Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged experiments. Pervasive and Mobile Computing, (2010).
- 66. initial study • 10 homes • staged calibration Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244. Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged experiments. Pervasive and Mobile Computing, (2010).
- 67. initial study • 10 homes • staged calibration • ~98% accuracy at identifying fixtures Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244. Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged experiments. Pervasive and Mobile Computing, (2010).
- 69. 70 50 30 pressure(psi) 70 50 30 pressure(psi) initial study: staged events kitchen sink kitchen sink
- 71. how well does HydroSense work in a natural setting?
- 77. totals days 33 33 30 27 33 156 events 2374 3075 4754 2499 2578 14,960 events/day 71.9 93.2 158.5 92.6 78.1 95.9 compound 22.2% 21.8% 16.6% 32% 21.3% 21.9% data collection Larson, E., Froehlich, J., Saba, E., et al. A Longitudinal Study of Pressure Sensing to Infer Real- World Water Usage Events in the Home. Pervasive Computing, Springer (2011), 50–69.
- 78. totals days 33 33 30 27 33 156 events 2374 3075 4754 2499 2578 14,960 events/day 71.9 93.2 158.5 92.6 78.1 95.9 compound 22.2% 21.8% 16.6% 32% 21.3% 21.9% data collection Larson, E., Froehlich, J., Saba, E., et al. A Longitudinal Study of Pressure Sensing to Infer Real- World Water Usage Events in the Home. Pervasive Computing, Springer (2011), 50–69. most comprehensive labeled dataset of hot and cold water ever collected
- 79. labeled natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi)
- 80. kitchen sink kitchen sink toilet bathroom sink labeled natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi)
- 81. kitchen sink kitchen sink toilet bathroom sink labeled natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) template matching: 98% 70%
- 82. kitchen sink kitchen sink toilet bathroom sink labeled natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) template matching: 98% 70% 10 fold cross validation
- 83. a new approach
- 84. a new approach templates feature vectors
- 85. a new approach templates feature vectors matching statistical approach
- 86. a new approach templates feature vectors matching statistical approach minimal training
- 91. feature vectors
- 92. feature vectors
- 93. feature vectors
- 95. 10 psi 7.32 psi feature vectors
- 96. 10 psi 7.32 psi 15 Hz feature vectors
- 97. 10 psi 7.32 psi 15 Hz 200 ms feature vectors
- 98. 10 psi 7.32 psi 15 Hz 200 ms feature vectors
- 99. 10 psi 7.32 psi 15 Hz 200 ms feature vectors
- 105. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 statistical model observed hidden
- 106. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 statistical model p(x, y) = TY t=1 p(yt|yt 1) | {z } transition p(xt|yt) | {z } emission observed hidden
- 107. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 supervised training p(x, y) = TY t=1 p(yt|yt 1) | {z } transition p(xt|yt) | {z } emission observed observed
- 108. calibration, per home a few days of training labels
- 109. calibration, per home a few days of training labels remainder of data is test set
- 111. 0 2 4 6 8 10 12 14 16 40 50 60 70 80 90 100 fixturelevelaccuracy(%) training amount (days)
- 112. 0 2 4 6 8 10 12 14 16 40 50 60 70 80 90 100 fixturelevelaccuracy(%) training amount (days) ideally
- 113. 0 2 4 6 8 10 12 14 16 40 50 60 70 80 90 100 fixturelevelaccuracy(%) training amount (days) ideally error bars = ±error bars
- 114. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 p(x, y) = TY t=1 p(yt|yt 1) | {z } transition p(xL t |yt) | {z } emission p(xU t ) | {z } unlabeled semi-supervised training
- 115. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 semi-supervised training p(x, y) = TY t=1 p(yt|yt 1) | {z } transition p(xL t |yt) | {z } emission p(xU t ) | {z } unlabeled
- 116. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 semi-supervised training p(x, y) = TY t=1 p(yt|yt 1) | {z } transition p(xL t |yt) | {z } emission p(xU t ) | {z } unlabeled
- 117. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 semi-supervised training p(x, y) = TY t=1 p(yt|yt 1) | {z } transition p(xL t |yt) | {z } emission p(xU t ) | {z } unlabeled
- 118. 40 50 60 70 80 90 100 0 2 4 6 8 10 12 14 16 fixturelevelaccuracy(%) training amount (days) error bars = ±error bars
- 119. 40 50 60 70 80 90 100 0 2 4 6 8 10 12 14 16 fixturelevelaccuracy(%) training amount (days) error bars = ±error bars
- 120. y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 decoding: add pairing
- 121. 1 y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 decoding: add pairing
- 122. 11111 y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 decoding: add pairing
- 123. 40 50 60 70 80 90 100 0 2 4 6 8 10 12 14 16 fixturelevelaccuracy(%) training amount (days) error bars = ±error bars
- 124. 40 50 60 70 80 90 100 0 2 4 6 8 10 12 14 16 fixturelevelaccuracy(%) training amount (days) error bars = ±error bars
- 130. Static E-Field HydroSenseHydroSense • low cost • easily installed • accurate • quickly calibrated • potential for high impact
- 133. how can indirect sensing and machine learning be used for health?
- 134. SpiroSmart a smartphone based spirometer that leverages on-device microphone to help you keep track of your lung function.
- 135. SpiroSmart a smartphone based spirometer that leverages on-device microphone to help you keep track of your lung function.lung function spirometer
- 136. spirometer device that measures amount of air inhaled and exhaled.
- 137. lung function asthma COPD cystic fibrosis evaluates severity of pulmonary impairments
- 143. volume-time graph volume time FEV1 FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity
- 144. volume-time graph volume time1 sec. FEV1 FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity
- 145. volume-time graph volume time1 sec. FEV1 FVC FEV1% = FEV1/FVC FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity
- 146. FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity FEV1% = FEV1/FVC
- 147. FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity FEV1% = FEV1/FVC > 80% healthy 60 - 79% mild 40 - 59% moderate < 40% severe
- 150. flow volume FEV1 FVC 1 sec. PEF PEF: Peak Expiratory Flow FEV1: Forced Expiratory Volume in 1 second FVC: Forced Vital Capacity flow-volume graph
- 153. obstructive diseases resistance in air path leads to reduced air flow
- 154. obstructive diseases resistance in air path leads to reduced air flow
- 155. restrictive diseases lungs are unable to pump enough air and pressure
- 156. restrictive diseases lungs are unable to pump enough air and pressure
- 159. clinical spirometry
- 160. home spirometry
- 161. home spirometry faster detection rapid recovery trending
- 162. home spirometry high cost barrier patient compliance less coaching limited integration challenges with
- 165. flow rate volume lung function airflow sensor sound pressure microphone processing estimated
- 166. SpiroSmart availability cost portability more effective coaching interface integrated uploading
- 167. Using SpiroSmart
- 168. Using SpiroSmart
- 169. Using SpiroSmart ]
- 170. Using SpiroSmart ]
- 171. Using SpiroSmart ]
- 172. Using SpiroSmart
- 173. initial study design x 3 x 3
- 174. need for attachments
- 175. need for attachments
- 176. mouthpiece
- 177. sling
- 178. study design x 3 x 3
- 179. + study design x 3 x 3 x 3 + + x 3 x 3
- 180. + study design x 3 x 3 x 3 + + x 3 x 3
- 181. + study design x 3 x 3 x 3 + + x 3 x 3
- 182. + study design x 3 x 3 x 3 + + x 3 x 3
- 183. + study design x 3 x 3 x 3 + + x 3 x 3
- 184. dataset + + +
- 185. participants 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + +
- 186. participants 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + +
- 187. participants 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + +
- 188. participants 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + +
- 189. participants 52 duration 45 minutes first session 29 abnormals 12 revisits 10 dataset + + +
- 190. audio
- 191. audio flow features
- 195. 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) audio flow features measures regression curve regression FEV1 FVC PEF
- 196. 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) audio flow features measures regression curve regression lung function FEV1 FVC PEF
- 197. 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 time(s) amplitude flow features
- 198. 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
- 199. 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
- 200. 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
- 201. 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
- 202. 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
- 203. 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
- 204. 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
- 205. 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
- 206. 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
- 207. 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
- 208. 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
- 209. 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
- 211. −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
- 212. −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
- 213. −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 PEF featuresground truth feature 1 feature 2
- 214. −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
- 215. −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 FEV1 features PEF featuresground truth feature 1 feature 2
- 216. −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
- 217. measures regression FEV1 features PEF features
- 218. measures regression FEV1 features bagged decision tree PEF features bagged decision tree
- 219. measures regression FEV1 features bagged decision tree output PEF features bagged decision tree output
- 221. results measures regression FEV1 FVC FEV1% PEF 0 2 4 6 8 10 Mean%Error Lower is better
- 222. results measures regression FEV1 FVC FEV1% PEF 0 2 4 6 8 10 Mean%Error Lower is better
- 223. results measures regression FEV1 FVC FEV1% PEF 0 2 4 6 8 10 No Personalization Personalization Mean%Error Lower is better
- 224. results measures regression general model = 8.8% error ATS criteria = 5-7% personal model = 5.1% error
- 225. results measures regression general model = 8.8% error ATS criteria = 5-7% normal attachments have no effect personal model = 5.1% error
- 227. 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
- 228. −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
- 229. 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
- 230. 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
- 231. 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)
- 232. 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)
- 233. 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)
- 234. 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)
- 235. 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)
- 236. can spirosmart curves be used for diagnosis?
- 237. survey
- 238. • 10 subjects curves survey
- 239. • 5 pulmonologists • 10 subjects curves survey
- 240. • 5 pulmonologists • 10 subjects curves • unaware if from SpiroSmart / spirometer survey
- 241. survey
- 242. survey
- 243. survey
- 244. survey
- 245. survey
- 246. results normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate identical 32 / 50
- 247. results normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate identical one off 32 / 50 5 / 18
- 248. results normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate identical one off 32 / 50 5 / 18
- 249. results normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate identical one off 32 / 50 5 / 18
- 250. results normal minimal obstructive mild obstructive moderate obstructive severe obstructive restrictive inadequate identical one off 32 / 50 5 / 18
- 251. FDA study underway
- 252. • part a: head to head clinical test FDA study underway
- 253. • part a: head to head clinical test • part b: home spirometry FDA study underway
- 257. thermal imaging facial analysis image fidelity power harvesting gas sensing cough sensing interaction& imageanalysis sustainabilityhealth water sensing lung function activity detection elder care daily activity logs congestive heart failure
- 258. thermal imaging facial analysis image fidelity power harvesting gas sensing cough sensing interaction& imageanalysis sustainabilityhealth water sensing lung function activity detection elder care daily activity logs congestive heart failure health markers via phone blood pressure pulse oximetry stress
- 259. thermal imaging facial analysis image fidelity power harvesting gas sensing cough sensing interaction& imageanalysis sustainabilityhealth water sensing lung function activity detection elder care daily activity logs congestive heart failure health markers via phone blood pressure pulse oximetry stress opportunistic sensing pain management detecting circulation
- 260. thermal imaging facial analysis image fidelity power harvesting gas sensing cough sensing interaction& imageanalysis sustainabilityhealth water sensing lung function activity detection elder care daily activity logs congestive heart failure health markers via phone blood pressure pulse oximetry stress opportunistic sensing pain management detecting circulation developing world
- 261. summary •sustainable water use, eco-feedback •lung function via mobile phone •future work in high impact areas
- 262. enabling eco-feedback and out-of-clinic health sensing indirect University of Washington Eric C. Larson UbiComp Lab electrical engineering computer science and engineering ubiquitous sensing eclarson.com eclarson@uw.edu @ec_larson Thank You! thermal imaging facial analysis image fidelity power harvesting gas sensing cough sensing interaction& imageanalysis sustainabilityhealth water sensing lung function
- 264. enabling eco-feedback and out-of-clinic health sensing indirect University of Washington Eric C. Larson PhD Candidate in School of Electrical and Computer Engineering UbiComp Lab electrical engineering computer science ubiquitous sensing eclarson.com eclarson@uw.edu @ec_larson acknowledgments: Jon Froehlich Leah Findlater Elliot Saba Eric Swanson Tim Campbell Gabe Cohn Mayank Goel TienJui Lee Sidhant Gupta Josh Peterson Conor Haggerty Jeff Beorse Shwetak Patel Les Atlas James Fogarty Jeff Bilmes Margaret Rosenfeld
- 271. water tower water tower thermal expansion tank hose spigot utility water meter pressure regulator laundry bathroom 1 hot water heater bathroom 2 dishwasher incoming cold water from supply line kitchen
- 272. water tower water tower thermal expansion tank hose spigot utility water meter pressure regulator laundry bathroom 1 hot water heater bathroom 2 dishwasher incoming cold water from supply line kitchen
- 277. stable pressure (psi) maxamplitude features: single instance
- 278. stable pressure (psi) maxamplitude Kitchen(Sink( Basement(Sink( Bathroom(Sink( Basement( Shower( ((((((((((((((((((((Upstairs(Shower( Upstairs(Bath( features: single instance
- 279. 42# 44# 46# 48# 50# 0# 5# 10# 15# 2 raw pressure (psi) 44" 46" 48" smoothed pressure (psi) bandpass derivative (psi/s) !4# 0# 4# detrended derivative bandpass derivative const-Q cepstrum signaltransforms features: single instance
- 280. frequency 42# 44# 46# 48# 50# 0# 5# 10# 15# 2 raw pressure (psi) *10# 10# 30# cepstral amplitude index detrended derivative bandpass derivative const-Q cepstrum signaltransforms features: single instance
- 287. 70 50 30 pressure(psi) 70 50 30 pressure(psi) 10 sec compound feature extraction: sequence
- 288. 70 50 30 pressure(psi) 70 50 30 pressure(psi) 10 sec compound time of day feature extraction: sequence
- 289. accuracy
- 290. fixture level e.g., upstairs bathroom faucet accuracy
- 291. fixture level e.g., upstairs bathroom faucet accuracy fixture 50 60 70 80 90 100 92.2
- 292. fixture level e.g., upstairs bathroom faucet accuracy fixture 50 60 70 80 90 100 92.2 what does this tell us? 10 fold cross validation
- 296. cycles
- 297. cycles heated dry
- 300. ~7:30AM
- 301. filling coffee pot coffee pot running ~7:30AM
- 303. bathroom usage
- 304. bathroom usage leaky flapper valve
- 305. flow features 0 1 2 3 4 −1 0 1 0 1 2 3 4 −0.5 0 0.5 Amplitude 0 1 2 3 4 −1 0 1 time(s)
- 306. flow features 0 1 2 3 4 −1 0 1 0 1 2 3 4 −0.5 0 0.5 Amplitude 0 1 2 3 4 −1 0 1 time(s) −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s)
- 307. flow features 0 1 2 3 4 −1 0 1 0 1 2 3 4 −0.5 0 0.5 Amplitude 0 1 2 3 4 −1 0 1 time(s) ~audio, pressure −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s)
- 308. flow features 0 1 2 3 4 −1 0 1 0 1 2 3 4 −0.5 0 0.5 Amplitude 0 1 2 3 4 −1 0 1 time(s) ~audio, pressure ~pressure at lips −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s)
- 309. flow features 0 1 2 3 4 −1 0 1 0 1 2 3 4 −0.5 0 0.5 Amplitude 0 1 2 3 4 −1 0 1 time(s) ~audio, pressure ~pressure at lips ~flow at lips −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s)
- 310. −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 linear chain regression
- 311. −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 feature 1 feature 2 curve output linear chain regression
- 312. CRF −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 linear chain regression
- 313. CRF −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 bagged decision tree linear chain regression
- 314. CRF −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 bagged decision tree −1 0 1 2 3 4 5 0 2 4 6 8 time(s) flow(L/s) linear chain regression
- 315. example curvescurve regression 0 1 2 3 0 2 4 6 8 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 10 volume(L) flow(L/s)
- 316. example curvescurve regression 0 1 2 3 0 2 4 6 8 volume(L) flow(L/s) 0 1 2 3 0 2 4 6 8 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 10 volume(L) flow(L/s) −1 0 1 2 3 4 0 2 4 6 8 10 volume(L) flow(L/s)