Blog post by Marco Altini
In this post I'll cover the data science and modeling behind the next version of the bedtime detection algorithms in HRV4Training. As current users know already, we hadn't put much effort in detecting bedtime until now. In this post I'll cover a few methods I've been working on in he past few days and highlight how combining noisy data sources using a simple Bayesian model can provide good results in detecting bedtime. The new model will be part of HRV4Training approximately around mid April.
How can we detect bedtime?
Last week I started looking at this problem more closely and developed a series of algorithms that I will now describe with the aim of understanding limitations and possible improvements that led to the current implementation based on a simple Bayesian model. Here are the models we will be comparing:
A few years back, for another app, I wrote an algorithm that was trying to detect bedtime based on Pedometer output. All iPhones starting with the 5 include a dedicated processor to count steps (that's why, for example, you can see your daily steps in the Health app without bothering about battery drain). My thought was, since the pedometer can detect movement, it can also detect lack of movement, and therefore we might be able to detect when people are sleeping. Once we go that way, we also make some assumptions, e.g. users pick up the phone first thing in the morning, and most importantly users bring the phone to their bedroom before going to bed. The drawing below shows the rationale behind this approach:
However, I was never happy about the output of this algorithm, as it could fail often. The pedometer is not perfect and sometimes it doesn't detect steps. Some other times I would simply leave the phone next to my bed hours before actually going to sleep. Especially when you live in a small place (see for example our current living situation), and don't walk that much to get to your bedroom, the algorithm can easily fail as no steps are detected.
As a result, it was accurate only sometimes and I decided not to deploy these algorithms in HRV4Training. However, as I've beed automating more and more of the daily tags in HRV4Training, for example your workouts data using Strava's APIs, I went back to the bedtime problem and looked at a few alternatives.
Moving average of your historical data
A simple alternative is to use historical data. For example, a moving average of your last week. As you annotate your bedtime for a few days, the app could learn from your input and start providing your recent average bedtime as the default one to edit. We are creatures of habit, and as such, our bedtime might be similar across days.
This approach is simple, makes sense, and will most likely get close to your actual bedtime. If the goal is to "limit the amount of manual editing", it might be good enough. However, unless we always go to bed at the exact same time, we will never get the estimate exactly right.
Bayesian model combining Pedometer output and historical data
Both our previous models suffer from limitations:
Bayes to the rescue
The current problem suits well a Bayesian framework. We have noisy data, historical annotations, and new evidence we gather daily based on Pedometer output. We can define a simple Bayesian model with normal conjugate priors to determine bedtime. In a Bayesian framework, we have three entities of interest; the prior, the likelihood, and the posterior:
Let's try to make this a bit more practical by looking at my recent data. We will assume we have one week of historical data with manual bedtime annotations. Using this historical data we can determine the prior parameters. The parameters of a normal distribution are the mean and variance, that we can simply derive from our initial week of recordings.
Another parameter we need in order to proceed, is the variance of our likelihood. The variance of our likelihood basically represents how much we trust our Pedometer output. I will postpone the discussion on the choice of this parameter to later, as I think looking at some data first can help in better understanding what we are doing here:
In the figure above, we can see the posterior, and therefore our model estimate, for three different hypothesis. We are answering the question: what would this model predict if the Pedometer output showed that we went to bed at 11pm? Similarly, what would this model predict if the Pedometer output showed that we went to bed at midnight or 1 am?
The x axis shows time of the day (hour) with respect to midnight, so -1 is 11 pm. On the y axis we have the density of the distributions.
My data shows a prior quite peaked just before midnight, meaning that I typically go to bed around that time, as our prior comes from my historical data. The likelihood (second row), is centered around our data point, so 11pm, midnight or 1am, and shows high variance. The distribution is quite spread out because we don't trust much our Pedometer output. Coming back to the variance of the likelihood, this parameter was estimated as the variance of the difference between the actual bedtime (manually annotated) and the Pedometer output estimated bedtime. If the accelerometer Pedometer was always correct (or almost always correct), we would have a much lower variance and trust more our likelihood, as I will show in another example later.
As a result, the posterior is always very close to the prior. The distribution on the third row changes only slightly, moving to the right, as we see evidence of going to bed later (11 pm to 1am). This is exactly what we want, as we trust more historical data than Pedometer output, given my particular case (see later to understand how this naturally generalizes to each individual).
Simulated data: sticking to our habits
As a second example, I want to show what would happen if we were really good at having a consistent lifestyle and going to bed every day pretty much at the same time. In this case, the Pedometer output would basically be completely ignored because we know we can rely much more on historical data. The likelihood is even more spread out, and the prior even more peaked than before:
Simulated data: trusting the Pedometer
Finally, as a third case, let's hypothesize we can actually trust our Pedometer. There might be individual cases in which you always carry your phone to bed, and maybe have to climb a flight of stairs on your way to the bedroom. This seems a case in which your phone might be good at determining bedtime, or at least better than your historical data. Since we trust the Pedometer more, the variance on the likelihood is much reduced, and our posterior follows the likelihood more than the prior, showing that the estimate will be much closer to the Pedometer output than the historical data:
We can see from the posterior plots above how for the first time we definitely move farther away from midnight as the data also moves away (11pm or 1am cases).
How good is this Bayesian model?
Now that I have explained the different models, and how the Bayesian model relies on both historical data and new evidence gathered from the phone Pedometer, we need to look at what all of this means for a user that simply wants to track sleep time without having to spend too much time annotating or editing tags in the morning.
Using my last week of data, I computed absolute errors for the following models:
The Historical data approach provides also decent results, showing that I tend to go to bed around the same time every day.
Finally, the Bayesian model further reduces error and provides the best estimates, just 10 minutes off the actual bedtime on average.
In this post I covered a few different methods to detect bedtime, proposing a Bayesian model able to combine noisy data sources for optimal results.
One of the best aspects of this model, is that it adapts to your own case. As I've shown above, confidence in historical or Pedometer data will change based on your own annotations and data. Thus, the algorithm will tune based on your behavior.
We'll be rolling out this feature soon, and hopefully make your life easier for the ones interested in tracking sleep time and bedtime.
If you are an iPad user or have an iphone 4S, as these devices do not have Pedometers, we will be implementing our second best model, i.e. the one using historical data.
Register to the mailing list
and try the HRV4Training app!
1. Intro to HRV
2. How to use HRV, the basics
3. HRV guided training
4. The big picture
5. HRV and training load
6. HRV, strength & power
7. Overview in HRV4Training Pro
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. rMSSD vs SDNN
7. Normal values and historical data
1a. Acute Changes in HRV
1b. Acute Changes in HRV (population level)
1c. Acute Changes in HRV & measurement consistency
1d. Acute Changes in HRV in endurance and power sports
2a. Interpreting HRV Trends
2b. HRV Baseline Trends & CV
3. Tags & Correlations
4. Ectopic beats & motion artifacts
5. HRV4Training Insights
6. HRV4Training & Sports Science
7. HRV & fitness / training load
8. HRV & performance
9. VO2max models
10. Repeated HRV measurements
11. VO2max and performance
12. HR, HRV and performance
13. Training intensity & performance
14. Publication: VO2max & running performance
15. Estimating running performance
16. Coefficient of Variation
17. More on CV and the big picture
18. Case study marathon training
Camera & Sensors
1. ECG vs Polar & Mio Alpha
2a. Camera vs Polar
2b. Camera vs Polar iOS10
2c. iPhone 7+ vs Polar
2d. Comparison of PPG sensors
3. Camera measurement guidelines
4. Validation paper
5. Android camera vs Chest strap
6. Zoom HRV vs Polar
7. Apple Watch and HRV
8. Scosche Rhythm24
9. Apple Watch
1. Features and Recovery Points
2. Daily advice
3. HRV4Training insights
4. Sleep tracking
5. Training load analysis
6a. Integration with Strava
6b. Integration with TrainingPeaks
6c. Integration with SportTracks
6d. Integration with Genetrainer
6e. Integration with Apple Health
6f. Integration with Todays Plan
7. HRV4T Coach advanced view
8. Acute HRV changes by sport
9. Remote tags in HRV4T Coach
10. VO2max Estimation
11. Acute stressors analysis
12. Training Polarization
13. Custom desirable range / SWC
14. Lactate Threshold Estimation
15. Functional Threshold Power(FTP) Estimation for cyclists
16. Aerobic Endurance analysis
17. Intervals Analysis
18. Training Planning
19. Integration with Oura
20. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV by sport
3. HRV normalization by HR
4. HRV 101