Hi. My name is Pegah and it's my pleasure to present our paper entitled Wearable Computing
Technology for Assessment of Cognitive Functioning of Bipolar Patients and Healthy Controls.
Human cognition has different aspects and the most important ones are attention, memory,
and executive function. Cognitive assessment is performed in clinics using neuropsychological tests.
Impairment in cognitive functioning is common in patients with bipolar disorder and it causes
problems in their daily life activities. For example they can become forgetful or unfocused.
Neuropsychological tests can be only administered in the clinics and they
require direct supervision of clinicians However, wearable technology can be used outside clinics
and they do not require direct supervision. In addition,
wearable technology provide a thorough assessment of cognitive functioning assessment
throughout a day and they also support mobile sensing for digital phenotyping.
We used our tool called UbiCAT in this paper to collect data.
UbiCAT is a smartwatch-based implementation of three cognitive tests
that measure attention, working memory, and executive function.
The objective of this paper is to demonstrate concurrent validity of UbiCAT, to show feasibility
of UbiCAT for in-the-wild cognitive assessment, to find the correlation between the sleep and working
memory of the study participants, to identify digital phenotypes of human mental health,
and to classify patients and healthy controls using their daily multivariate data set.
We conducted an in-the-wild study with UbiCAT and the participants of this
study were patients with bipolar disorder and healthy controls. Participants were required to
take the recognitive test of UbiCAT three times per day during morning, afternoon, and evening.
The whole duration of the study was seven days per participants and during the study,
mobile sensor features of the participants were collected passively through their smartwatches and
their smartphones. Our findings show that UbiCAT has a demonstrated concurrent validity compared to
standard neuropsychological tests. Moreover, UbiCAT showed to be a feasible technology for
in-the-wild cognitive assessment since the cognitive performance of the participants
who took the test in indoor and outdoor places were not statistically different from each other.
Multivariate data of the participants were collected during the study including 76
observations of the patients and healthy controls and 11 features.
The features that were collected via passive sensing are time time in bed,
sleep duration, number of awakening, duration of awakening, and step count.
The cognitive test performance measures are test accuracy, response times, and
missed stimuli. The label of this data set is the mental health diagnosis of the participants.
Our analysis showed that bipolar patients and healthy controls who slept
more have a better working memory the next day. Bipolar patients have lower alertness
compared to the healthy controls. As you can see in this figure, bipolar patients
showed longer response times compared to healthy controls in several hours of the day.
We extracted digital phenotypes of mental health using our multivariate data set.
Bipolar patients showed a statistically different performance in their cognitive, physiological,
and behavioral features including missed stimuli, response times in attention and executive
functioning test of UbiCAT, time in bed, sleep duration, awakening features and step counts.
We applied feature selection using Principal Component Analysis before creating supervised
models of human mental health. The features that were selected using these methods are timing bed,
sleep duration, step count, accuracy and response times of UbiCAT cognitive tests.
We applied k-nearest neighborhood, random forest, support vector machine, and XGBoost to our
multivariate dataset to predict the presence or absence of mental disorder. We used leave
one subject out and 5-fold cross-validation for training. As you can see in the table k-nearest
neighbor achieved the highest accuracy among the rest of the classification methods in this table.
We conclude that UbiCAT is a feasible technology for in-the-wild cognitive assessment
especially when users take different positions such as sitting, standing,
and walking. We also monitored mental health of the participants that showed that bipolar
patients have different patterns in terms of their cognitive, behavioral, and physiological measures.
This also later led us to identify digital phenotypes. We also conclude that UbiCAT
can predict the absence and presence of mental disorder using a multivariate data
set that can be used for early diagnosis and treatment of bipolar disorder patients.
I would like to thank the funding of this project and a special thanks to our study participants.
Thank you for your attention. For more information
please read our paper and I would be happy to stay in touch.