Data Science in #mHealth @neal_lathia @Cambridge_Uni @emotionsense

Many data science applications deal with some kind of behaviour.

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

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

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

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

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

No content

No content

No content

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

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

angry anxious lonely relaxed enthusiastic calm @EmotionSense has multiple assessments for mood and life satisfaction

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

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

Accelerometer Data ● 109,054,559 samples collected in f i rst 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)?

No content

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

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

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)

Week day, k = 2 Visualising Centroids Example (Non-f i nal) Result Week end, k = 2

Visualising by heat map Example (Non-f i nal) Result Week day Week end

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')

No content

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?

Conclusions

Data Science in #mHealth @neal_lathia @Cambridge_Uni @emotionsense