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Opportunities and Challenges of 
Using Smartphones for Health 
Monitoring and Intervention 
@neal_lathia 
Computer Laborat...
http://en.wikipedia.org/wiki/Macintosh_SE My first computer.
M y latest computer .
Macintosh SE 
1987 
8 MHz 
1 MB 
20 MB 
7.7 kg 
$2,900 
Model: 
Released: 
Processor: 
RAM: 
Hard Drive: 
Weight: 
Price: ...
1/5: the “basics” 
why health via smartphones? 
why smartphones for health?
why health via smartphones? 
health “in the moment” vs. “reconstructed” 
ubiquity of technology vs. limited face-to-face
Smartphones are incredibly personal devices: 
they are not often shared. 
Research shows that owners regularly keep their ...
why smartphones for health? 
2/5: interactivity 
3/5: sensors 
4/5: machine learning
2/5: interactivity * 
notifications 
check-ins 
games 
situation-awareness 
momentary, context-aware, engaging 
* I don't ...
notifications
check-ins
games
situation-aware
Emotion Sense Q Sense 
mood tracking smoking cessation 
Dept. of Psychology Behavioural Sciences
check-ins 
'volunteering'
check-ins 
'volunteering'
notifications 
'prompting'
“games” 
'guiding'
situation-aware 
'informing'
2/5: interactive apps 
a) active behavioural monitoring 
b) momentary assessment 
c) information delivery
3/5: smartphone sensors 
p assive behavioural monitoring
Sensors were originally added to smartphones 
for purely functional purposes. 
E.g., an accelerometer lets the device know...
what is a “sensor?” 
Accelerometer 
GPS / Wi-Fi 
Gyroscope 
Bluetooth 
Microphone 
Environment 
Phone / Text Logs 
Device ...
Accelerometer → activity 
GPS / Wi-Fi → location, mobility 
Gyroscope → device orientation 
Bluetooth → co-location 
Micro...
Sensors do not “directly” encode behaviour. For 
example, sampling from the accelerometer 
provides time series data of ch...
raw sensor data 
features
does the accelerometer feature correlate with 
reports of current levels of physical activity? 
r = 0.369 
does the accele...
...batteries are still headaches: 
Sensors were originally added to smartphones 
for purely functional purposes. 
These se...
4/5: machine learning 
u sing data to infer behaviour
Machine Learning (vs. Behaviour Theory?) 
Behaviours are often too complex and/or 
abstract to directly encode them into s...
Two broad categories of learning algorithms, 
which are often referred to as unsupervised and 
supervised learning.
Unsupervised learning, or clustering, assumes 
you have: 
(a) a large dataset of many representations of a 
behaviour, and...
What are the behaviours that emerge when a city 
uses the stations in a bicycle sharing scheme? 
Define a way of represent...
What are the behaviours that emerge when a city 
uses the stations in a bicycle sharing scheme? 
Define a way of comparing...
What are the behaviours that emerge when a city 
uses the stations in a bicycle sharing scheme?
What diurnal patterns of physical activity emerge 
from smartphone accelerometers?
What diurnal patterns of physical activity emerge 
from smartphone accelerometers? 
User A ... 1.23 2.33 3.12 
... 7AM 8AM...
What diurnal patterns of physical activity emerge 
from smartphone accelerometers? 
User A ... 1.23 2.33 3.12 
... 
User B...
What diurnal patterns of physical activity emerge 
from smartphone accelerometers? 
How does this relate to happiness?
4/5: machine learning 
u sing data to infer & predict behaviour 
* I didn't include supervised learning, which is awesome
5/5: challenges & opportunities
1. Software Engineering / Expectations 
2. Marketing 
3. Control over target population 
4. Understanding sensor data 
5. ...
1. Blurred lines between research and practice 
2. High potential for multi-disciplinary impact 
3. Cheap to roll-out to h...
Opportunities and Challenges of 
Using Smartphones for Health 
Monitoring and Intervention 
@neal_lathia 
Computer Laborat...
“Can I run an ESM study 
like Emotion Sense?”
Opportunities and Challenges of Using Smartphones for Health Monitoring and Intervention
Opportunities and Challenges of Using Smartphones for Health Monitoring and Intervention
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Opportunities and Challenges of Using Smartphones for Health Monitoring and Intervention

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A recent talk about designing, building, and analysing the data from Emotion Sense and Q Sense -- Android apps for health monitoring and intervention.

Published in: Healthcare
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Opportunities and Challenges of Using Smartphones for Health Monitoring and Intervention

  1. 1. Opportunities and Challenges of Using Smartphones for Health Monitoring and Intervention @neal_lathia Computer Laboratory University of Cambridge
  2. 2. http://en.wikipedia.org/wiki/Macintosh_SE My first computer.
  3. 3. M y latest computer .
  4. 4. Macintosh SE 1987 8 MHz 1 MB 20 MB 7.7 kg $2,900 Model: Released: Processor: RAM: Hard Drive: Weight: Price: Samsung Galaxy S4 2013 1.6 GHz x 4 2 GB 16/32 GB 130 g ~$500
  5. 5. 1/5: the “basics” why health via smartphones? why smartphones for health?
  6. 6. why health via smartphones? health “in the moment” vs. “reconstructed” ubiquity of technology vs. limited face-to-face
  7. 7. Smartphones are incredibly personal devices: they are not often shared. Research shows that owners regularly keep their smartphone within arms length of them. A. Dey et. al. Getting Closer: An Empirical Investigation of the Proximity of Users to their Smartphones. In ACM Ubicomp 2011.
  8. 8. why smartphones for health? 2/5: interactivity 3/5: sensors 4/5: machine learning
  9. 9. 2/5: interactivity * notifications check-ins games situation-awareness momentary, context-aware, engaging * I don't research interaction/design
  10. 10. notifications
  11. 11. check-ins
  12. 12. games
  13. 13. situation-aware
  14. 14. Emotion Sense Q Sense mood tracking smoking cessation Dept. of Psychology Behavioural Sciences
  15. 15. check-ins 'volunteering'
  16. 16. check-ins 'volunteering'
  17. 17. notifications 'prompting'
  18. 18. “games” 'guiding'
  19. 19. situation-aware 'informing'
  20. 20. 2/5: interactive apps a) active behavioural monitoring b) momentary assessment c) information delivery
  21. 21. 3/5: smartphone sensors p assive behavioural monitoring
  22. 22. Sensors were originally added to smartphones for purely functional purposes. E.g., an accelerometer lets the device know when to display the screen in landscape mode; the GPS allows the device to support maps/driving apps. Only later did researchers uncover that all of these sensors could be a valuable source of behavioural data.
  23. 23. what is a “sensor?” Accelerometer GPS / Wi-Fi Gyroscope Bluetooth Microphone Environment Phone / Text Logs Device Logs Social Media APIs App Usage
  24. 24. Accelerometer → activity GPS / Wi-Fi → location, mobility Gyroscope → device orientation Bluetooth → co-location Microphone → audio processing Environment → light / temperature / pressure Phone / Text Logs → socialising Device Logs → network / battery Social Media APIs → socialising, connectivity App Usage → context, searching
  25. 25. Sensors do not “directly” encode behaviour. For example, sampling from the accelerometer provides time series data of changes in acceleration. All sensor data needs to be processed in order to extract/infer behaviours. How, for example, does the accelerometer indicate physical activity?
  26. 26. raw sensor data features
  27. 27. does the accelerometer feature correlate with reports of current levels of physical activity? r = 0.369 does the accelerometer feature correlate with reports of levels of physical activity on that day? r = 0.172
  28. 28. ...batteries are still headaches: Sensors were originally added to smartphones for purely functional purposes. These sensors were not built to efficiently collect continuous streams of data*. * This is changing...? K. Rachuri. Smartphones Based Social Sensing: Adaptive Sampling, Sensing and Computation Offloading. PhD Thesis, Computer Laboratory. 2012.
  29. 29. 4/5: machine learning u sing data to infer behaviour
  30. 30. Machine Learning (vs. Behaviour Theory?) Behaviours are often too complex and/or abstract to directly encode them into software. Machine learning a statistical approach that centres around using data to learn to identify and predict behaviours. Often without knowing much (or anything) about what those behaviours actually look like.
  31. 31. Two broad categories of learning algorithms, which are often referred to as unsupervised and supervised learning.
  32. 32. Unsupervised learning, or clustering, assumes you have: (a) a large dataset of many representations of a behaviour, and (b) a way of measuring the extent that two representations of behaviours are similar. … without knowing precisely how to code for that behaviour
  33. 33. What are the behaviours that emerge when a city uses the stations in a bicycle sharing scheme? Define a way of representing the behaviour: Station A ... 50% 25% 32% ... 7AM 8AM 11PM
  34. 34. What are the behaviours that emerge when a city uses the stations in a bicycle sharing scheme? Define a way of comparing behaviour: Station A ... 50% 25% 32% ... Station B ... 23% 34% 52%
  35. 35. What are the behaviours that emerge when a city uses the stations in a bicycle sharing scheme?
  36. 36. What diurnal patterns of physical activity emerge from smartphone accelerometers?
  37. 37. What diurnal patterns of physical activity emerge from smartphone accelerometers? User A ... 1.23 2.33 3.12 ... 7AM 8AM 11PM
  38. 38. What diurnal patterns of physical activity emerge from smartphone accelerometers? User A ... 1.23 2.33 3.12 ... User B ... 2.33 3.43 2.33
  39. 39. What diurnal patterns of physical activity emerge from smartphone accelerometers? How does this relate to happiness?
  40. 40. 4/5: machine learning u sing data to infer & predict behaviour * I didn't include supervised learning, which is awesome
  41. 41. 5/5: challenges & opportunities
  42. 42. 1. Software Engineering / Expectations 2. Marketing 3. Control over target population 4. Understanding sensor data 5. Writing code 6. Finding research value
  43. 43. 1. Blurred lines between research and practice 2. High potential for multi-disciplinary impact 3. Cheap to roll-out to huge audiences 4. Accessible to 'everyone' 5. Rising demand for quality healthcare technology 6. Wearables are coming!
  44. 44. Opportunities and Challenges of Using Smartphones for Health Monitoring and Intervention @neal_lathia Computer Laboratory University of Cambridge
  45. 45. “Can I run an ESM study like Emotion Sense?”

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