Hello, my name is Boning from Rice University.
I’m glad to present this work on the topic of
deep autoencoder based sensor features for
personalized prediction of mood, health, and stress.
As psychologists pointed out, mental health crisis
can be prevented with timely interventions.
According to a study in 2012,
about 1/3 major depression episodes were preventable with existing methods.
Our goal here is to help doctors determine the right timing of intervention.
We can achieve that by predicting accurate
wellbeing scores using passive sensing data.
Our data were collected by Affectiva Q-sensors,
8 hz signals in 3 channels, namely the
skin conductance, skin temperature, and acceleration.
For the ground truth, email surveys were
sent to participants at 5 pm every day,
asking for their self-assessed scores of mood, health, and stress
on a continuous 100-point slide bar.
Besides, there was also a pre-study screen
of participants’ gender and personality.
This information will be useful for
clustered personalization which we’ll give more details later.
To decompose the problem, we are essentially learning two projections.
The first mapping is from the raw sensor data
to the physiological and behavioral features,
the other from the features to the prediction of the wellbeing scores.
Autoencoders are often used for unsupervised feature learning.
It learns a compressed representation of the input data
by trying to reconstruct the original input
from the lower-dimensional latent space.
Our proposed model has bottom layers locally connected,
meaning that computations were restricted to separate time frames
It can avoid overparameterization and combat overfitting.
We aggregate static features for temporal features by
wrapping the hidden layers with a recurrent
encoder-decoder structure based on LSTM cells.
In prior to reconstructing the original data,
we added Gaussian noise to the learned representations
to train for more robust features.
For the representation model, the personalization is done by finetuning
the generalized representation learning model to each participant.
Now that we have the features,
we can move forward to personalized wellbeing prediction
with multi-task learning via structural regularization.
Essentially, this is a linear model with l-21 and l2 norm penalties.
Multi-task learning means we need to first define what a task is.
Basically, there are 2 ways of assumptions.
If you assume all users in the test set are also in the training set,
then we can do user-dependent split.
Otherwise, if any users in the test set
are never seen by the training set,
we have to do user-independent split.
To accommodate for unseen users,
we can make clusters of users as tasks based on their profiles.
So that the model can always assign
new users to the closest existing cluster
and treat them as a known task.
On interpretation, our goal is to let
the model give explanations to humans
so that we human can understand what features or
inputs are associated with negative mental status,
and so doctors can design interventions to fix them.
The representation models were evaluated by
the reconstruction loss, or the mean squared errors.
The prediction results were evaluated by the mean absolute errors.
Here’s the result of prediction errors vs the number of clusters
for the two personalization assumptions.
The overall tendencies were going down,
meaning that the performance was
improved by fine-grained personalization.
In the end, the individual-as-task prediction performed
significantly better than the group-as-task
prediction, in terms of health and stress.
This table compares the results of predication
performance using different sets of features,
the takeaway being that auto features worked better than crafted features;
temporal auto features worked better than static auto features;
and using 7 days of features generally
performed better than using 1 day features.
Although we did see improvement of personalizing features
in terms of the reconstruction loss,
unfortunately, we are not seeing that improvement
being transferred to the prediction here.
It could be that the individual differences in self-reports were
dominant over the individual differences in physiology.
Then we are on to the interpretation results.
In the correlation matrix between auto and crafted features,
where the correlations are high,
it indicates that the auto features are
closer to the corresponding crafted features.
Here we list several highly correlated pairs.
In this way, we can associate some
physical meaning with the auto features.
This work has limitations and room for improvement.
Our results were solely based on a college student dataset.
Re-validation on other datasets is needed in the future.
We’re also actively looking into other types of
data such as phone usage, location, and weather.
Further works are recommended to solve the privacy concerns
and make this proposed framework to operate in real-time.
In conclusion, our proposed temporal representation
framework can learn good wellbeing features.
Personalization is important to realize
the auto features’ predicting efficacy.
We also emphasize the interpretability via
statistical approaches and self-attention mechanism.
For more details, please check out our full paper.
Thank you