This document discusses using data science and machine learning techniques to analyze mobile health sensor data in order to better understand user behaviors and improve health outcomes. It describes how sensors in mobile devices can capture small, momentary behaviors and behaviors can be clustered and visualized using techniques like k-means clustering. Accelerometer data from the Emotion Sense app is analyzed as a case study, with results showing differences in activity patterns between weekdays and weekends. The document concludes that mobile sensors allow unprecedented observation of behaviors that could enable just-in-time interventions to change behaviors.

1. Data Science in #mHealth
@neal_lathia
@Cambridge_Uni
@emotionsense

2. Many data science applications
deal with some kind of behaviour.

3. Data science: making sense from
& putting big data sets of small
behaviours to use.
(e.g., ratings #recsys)

4. Many health settings relate to
one or more of our behaviours.

5. Improving health: changing our
small, momentary, cumulative
behaviours.
(e.g., one cigarette smoking cessation)

6. How can we capture our small,
momentary, cumulative
behaviours?

7. Accelerometer
Microphone
Camera
GPS
Compass
Gyroscope
Wi-Fi
Bluetooth
Proximity
NFC
Light

11. For the user, Emotion Sense is
about psychological wellbeing,
ref lection, momentary
assessment, and contextual
feedback.

12. What can we learn from the
data?
This talk: unsupervised Learning + viz, R + python

13. angry anxious lonely
relaxedenthusiasticcalm
@EmotionSense has multiple assessments for
mood and life satisfaction

14. Accelerometer
Microphone
Location
Wi-Fi
Call Logs
SMS Logs
* all anonymised, pre-
processed

15. Happiness Metric:
Many moments of positive
feelings, high satisfaction with life

16. Accelerometer Data
● 109,054,559 samples collected in f irst 12(ish)
months of public deployment from 14,810 users
● What 'emergent' behaviours exist in this data?
How does it characterise the users?
● How do these behaviours relate to external data
we have collected from the same users (i.e.,
mood)?

18. Accelerometer Samples Matrix
● Extract features from accelerometer samples
● Each sample has 3 axes (x, y, z)
● Each axis is a time series of data
● Various features can be extracted:
– Statistical
– Temporal
– Signal

19. from sklearn.cluster import KMeans
c = KMeans(init='k-means++', n_clusters=4)
c.fit(data)
result = method.labels_

20. plot(X, Y, col=grey(.5), type="l", axes=F, xlab="Time
of Day", ylab=ylab, main=title, ylim=c(0,1))
axis(1, labels=seq(from=0,to=24,by=1), at=seq(from=0,to=24,by=1))
axis(2)
box()
grid()
f <- rep(1/4, 4)
y_lag <- filter(Y, f, sides=2)
lines(X, y_lag, col="red", lwd=2)

21. Week day, k = 2
Visualising Centroids
Example (Non-f inal) Result
Week end, k = 2

22. Visualising by heat map
Example (Non-f inal) Result
Week day
Week end

23. Visualising by heat map (python)
plt.figure()
fig, ax = plt.subplots()
ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)
ax.set_xticklabels([i for i in xrange(0, 24)], minor=False)
ax.xaxis.set_tick_params(width=0)
ax.xaxis.tick_bottom()
ax.set_yticks([])
ax.set_yticklabels([])
plt.xlabel('Time of Day')
plt.title(p_title)
plt.grid(False)
ax.pcolormesh(data, cmap=plt.cm.hot)
savefig(filename+'.pdf')

25. Conclusions
● Tools
– Android, R, Python (multiprocessing), MongoDB
– https://github.com/nlathia/research-util
– https://github.com/xsenselabs
● What's haven't I talked about?
– Supervised learning (“what are you doing now?”)
– Other sensors
● Next Gen:
– Levels of behaviour that were never possible to
observe before; scale without wearables
– Potential to catch people “in the moment”
– Time to redesign behavioural interventions?

26. Conclusions

27. Data Science in #mHealth
@neal_lathia
@Cambridge_Uni
@emotionsense