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The rapid adoption of smartphones along with a growing habit for using these devices as alarm clocks presents an opportunity to use this device as a sleep detector. This adds value to UbiComp and personal informatics in terms of user context and new performance data to collect and visualize, and it benefits healthcare as sleep is correlated with many health issues. To assess this opportunity, we collected one month of phone sensor and sleep diary entries from 27 people who have a variety of sleep contexts. We used this data to construct models that detect sleep and wake states, daily sleep quality, and global sleep quality. Our system classifies sleep state with 93.06% accuracy, daily sleep quality with 83.97% accuracy, and overall sleep quality with 81.48% accuracy. Individual models performed better than generally trained models, where the individual models require 3 days of ground truth data and 3 weeks of ground truth data to perform well on detecting sleep and sleep quality, respectively. Finally, the features of noise and movement were useful to infer sleep quality.
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Toss ‘N’ Turn:Smartphone asSleep and Sleep Quality Detector
Jun-Ki Min (loomlike@cs.cmu.edu)Afsaneh DoryabJason WieseShahriyar AminiJohn ZimmermanJason I. Hong
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Sensing Sleep for…
• Personal informatics
• UbiComp system
• Health monitoring
3
Current Practices
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Opportunities
• We already have smartphones
• 83% of millennials sleep with their phonePew Internet
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How well a smartphone can sense sleep without requiring changes in our behavior?
Task 1. Detect bedtime, waketime and duration
Task 2. Infer daily sleep quality
Task 3. Classify good or poor sleeper
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Toss’N’Turn
Sound amplitude
Acceleration
Ambient light intensity
Screen proximity
Processes
Battery state
Screen state
Sleep diary
Data
pre
proc
essi
ng
Feat
ure
extr
acti
on
Database Server
Toss ‘N’ Turn (Data Collection Ver.)
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ModelingSoundMotion Sleep
Good or poor
Bedtime WaketimeDuration
…01000000111111111011101011000
…0000000011111111111111111100010-minute window
Sleeping (1) or not (0)
…00 …010
…000Every 30 minutes,run the sleep detection model
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User Study
• Recruited good and poor sleepers– Living in US, age > 18– Pay $2 USD for each diary entry (a maximum $72)
• Collected sleep data for a month
• 30 participants signed up and 27 completed– Total 795 sleep-diary entries
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Ground TruthingUser Study
Global score > 5 indicatesa subject is having poor
sleep
Subjective sleep quality+ Sleep latency
+ Sleep efficiency+ Sleep duration
+ Use of medication+ Sleep disturbances
----------------------------------= Global sleep quality
Demographics
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User Study
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Share bed with
3
8
3
1
1
Disrupting noises in the bedroom12
15YesNo
Age1010
511
20304050?
Regularly work22
5NoYes
Sex
19
8
Poor sleeper(PSQI global score > 5)
Good sleeper (PSQI global score ≤ 5)
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Evaluation
• Classifier– Bayesian network (BN) with correlation-based feature
selection
• Task 1. Detect bedtime, waketime and duration
• Task 2. Infer daily sleep quality– Train the model individually (leave-one-day-out cross
validation)
• Task 3. Classify good or poor sleeper– Leave-one-person-out cross validation
Task 1: Sleep Detection
• Detect sleep windows Detect sleep time
• 94.5% in classifying sleep/not-sleep windows
Evaluation
Bedtimedetection
Baseline (avg. time)
Our methodWaketimedetection
Baseline (avg. time)
Sleep durationinference
Baseline (avg. time)
-150 150-120 1209060300-30-60-90Average minutes of over (+) and under (-) estimation errors
Our method
Our method
Task 2: Daily Sleep Quality Inference
Evaluation
• Detect sleep Classify the quality of sleep
• 84.0% in classifying good/poor sleeps
Accuracy (%)
Our methodRandom
Poor sleep detection(F-score)
Task 3: Good/Poor Sleeper Classification
Evaluation
• Infer daily qualities Classify the sleeper type
• 81.5% in classifying good/poor sleepers
Our methodRandom
Accuracy (%) Poor sleeper detection(F-score)
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Discussion
How well a smartphone can sense sleep without requiring changes in our behavior?
Task 1. Detect bedtime, waketime and durationwithin 35, 31, and 49 minutes of errors, respectively
Task 2. Infer daily sleep quality with 84% accuracy
Task 3. Classify good or poor sleeper with 81% accuracy
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Top Five Features– Time– Battery charging / not-charging– Min. movement– Std. sound amplitude– Q3 sound amplitude
– Bedtime– Waketime– Sleep duration– Std. movement– Yesterday’s sleep quality
Discussion
Sleep detection
Sleep qualityinference
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Sleep Detection ErrorsDiscussion
People whosleep alone
People whohave a sleep partner
Phone location
Erro
r (m
inut
es)
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General vs. Individual Models
• Sleep detection: 93.06% vs. 94.52%– Need 3 days of ground truthing to train an individual
model
• Sleep quality inference: 77.23% vs. 83.97%– Need 3 weeks of ground truthing to train an individual
model
Discussion
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Limitations
• Subjective vs. objective sleep quality– “How was your sleep last night? Rate it on a one to five
scale score” does not capture the full extent of a sleep session
• People tend to over / underestimate their sleep
• Small sample size of poor-quality sleep
Discussion
Thanks!
• More info at cmuchimps.orgor email loomlike@cs.cmu.edu
• Special thanks to:– DARPA, Google
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Backup Slides
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Data Collection Frequency
Sensed value (frequency)Data collection cycle
Night Day Btry<30% Btry<15%
Sound amplitude (1hz) Cont. Every other minute
Stop
Acceleration (5hz) Cont. Every other minute
Light, screen proximity (1/5hz) Cont. Every other minute
List of running apps (1/10hz) When the screen is turned on
Battery states When the battery level is changed or the power cable is plugged in/out
Screen states When screen is turned on/off
Sleep diary Every morning (notification)
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FeaturesCategory Factor Feature variables
Sleep detection
(32 features
for each window)
Noise level Sound amplitudes{Min., Q1, Med., Q3, Max., Avg., Std.}
Movement Acceleration changes{Min., Q1, Med., Q3, Max., Avg., Std.}
Light intensity Light intensities & screen proximities{Min., Q1, Med., Q3, Max., Avg.,
Std.}
Device state & usage
Duration of screen-on time{Min., Q1, Med., Q3, Max., Avg., Std.},battery state{plug-in/out, charging/not-charging} & alarm app usage
Regular sleep time Timestamp
Daily quality
(122 features for
each sleep)
Sleep duration Bedtime, waketime & sleep duration (detected)
Sleep latency, efficiency & disturbances
Sensor values{#peaks, Avg. width of peaks, Avg. height of peaks, interval of peaks,
position of peaks, Min., Q1, Med., Q3, Max., Avg., Std.} &yesterday’s sleep quality (previously inferred)
Global quality
(198 features for
each participant)
Sleep regularityBedtimes, waketimes, sleep durations & qualities for a month of sleeps{Med., Avg., Std.} (previously detected and
inferred)Global sleep
latency, efficiency & disturbances
Daily sleep quality features{Med., Avg., Std.}
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Modeling: Data Processing
• Rational for “10 minutes”– Level of granularity when participants report sleep time– Median sleep latency = 10.9 minutes
• 90,097 windows,711 not-sleep and 728 sleep segments
…
SoundMotion
10-minute window
Sleep
Sleep Not-sleepNot-sleep Bedt
ime
Wak
etim
e
25Detection
Sleep timedetection model
Smoothing
Bedtime, Waketime, Duration
Every 30m
Sleep Detection & Quality Inference
Modeling
…
SoundMotion
10-minute window
Sleep
Classification
Sleep windowdetection model
Feature extraction
1 = sleep or0 = not-sleep
…0011000001010000011111111011010100001010000100000……0000000000000000011111111111111100000000000000000… Bedtime WaketimeDuration
Classification
Sleep qualityinference model
Feature extraction
Good or poor
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Infer Other Contexts
• Sleep alone vs. with others– 84.2%
• Phone on the bed vs. near the bed vs. elsewhere– 91.9%
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