9 research outputs found
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it
Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma
Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations
Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients
We aim to develop clustering models to obtain behavioral representations from
continuous multimodal mobile sensing data towards relapse prediction tasks. The
identified clusters could represent different routine behavioral trends related
to daily living of patients as well as atypical behavioral trends associated
with impending relapse.
We used the mobile sensing data obtained in the CrossCheck project for our
analysis. Continuous data from six different mobile sensing-based modalities
(e.g. ambient light, sound/conversation, acceleration etc.) obtained from a
total of 63 schizophrenia patients, each monitored for up to a year, were used
for the clustering models and relapse prediction evaluation. Two clustering
models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were
used to obtain behavioral representations from the mobile sensing data. The
features obtained from the clustering models were used to train and evaluate a
personalized relapse prediction model using Balanced Random Forest. The
personalization was done by identifying optimal features for a given patient
based on a personalization subset consisting of other patients who are of
similar age.
The clusters identified using the GMM and PAM models were found to represent
different behavioral patterns (such as clusters representing sedentary days,
active but with low communications days, etc.). Significant changes near the
relapse periods were seen in the obtained behavioral representation features
from the clustering models. The clustering model based features, together with
other features characterizing the mobile sensing data, resulted in an F2 score
of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation
setting. This obtained F2 score is significantly higher than a random
classification baseline with an average F2 score of 0.042
Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations
Hallucination is an apparent perception in the absence of real external
sensory stimuli. An auditory hallucination is a perception of hearing sounds
that are not real. A common form of auditory hallucination is hearing voices in
the absence of any speakers which is known as Auditory Verbal Hallucination
(AVH). AVH is fragments of the mind's creation that mostly occur in people
diagnosed with mental illnesses such as bipolar disorder and schizophrenia.
Assessing the valence of hallucinated voices (i.e., how negative or positive
voices are) can help measure the severity of a mental illness. We study N=435
individuals, who experience hearing voices, to assess auditory verbal
hallucination. Participants report the valence of voices they hear four times a
day for a month through ecological momentary assessments with questions that
have four answering scales from ``not at all'' to ``extremely''. We collect
these self-reports as the valence supervision of AVH events via a mobile
application. Using the application, participants also record audio diaries to
describe the content of hallucinated voices verbally. In addition, we passively
collect mobile sensing data as contextual signals. We then experiment with how
predictive these linguistic and contextual cues from the audio diary and mobile
sensing data are of an auditory verbal hallucination event. Finally, using
transfer learning and data fusion techniques, we train a neural net model that
predicts the valance of AVH with a performance of 54\% top-1 and 72\% top-2 F1
score
Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management
The increase of mental illness cases around the world can be described as an urgent
and serious global health threat. Around 500 million people suffer from mental disorders, among
which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological
paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess,
and care for patients early. This paper comprehensively survey works done at the intersection
between IoT and mental health disorders. We evaluate multiple computational platforms, methods
and devices, as well as study results and potential open issues for the effective use of IoT systems
in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT
solutions for mental health care, which can be relevant given the potential impairments in some
mental health patients such as data acquisition issues, lack of self-organization of devices and service
level agreement, and security, privacy and consent issues, among others. We aim at opening the
conversation for future research in this rather emerging area by outlining possible new paths based
on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program
PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine"
RTI2018-101674-B-I0
Quantified Canine: Inferring Dog Personality From Wearables
Being able to assess dog personality can be used to, for example, match
shelter dogs with future owners, and personalize dog activities. Such an
assessment typically relies on experts or psychological scales administered to
dog owners, both of which are costly. To tackle that challenge, we built a
device called "Patchkeeper" that can be strapped on the pet's chest and
measures activity through an accelerometer and a gyroscope. In an in-the-wild
deployment involving 12 healthy dogs, we collected 1300 hours of sensor
activity data and dog personality test results from two validated
questionnaires. By matching these two datasets, we trained ten machine-learning
classifiers that predicted dog personality from activity data, achieving AUCs
in [0.63-0.90], suggesting the value of tracking the psychological signals of
pets using wearable technologies.Comment: 26 pages, 9 figures, 4 table
Ending the Discriminatory Pretrial Incarceration of People with Disabilities: Liability under the Americans with Disabilities Act and the Rehabilitation Act
Our federal, state, and local governments lock up hundreds of thousands of people at a time—millions over the course of a year—to ensure their appearance at a pending criminal or immigration proceeding. This type of pretrial incarceration—a term we use to cover both pretrial criminal detention and immigration detention prior to finalization of a removal order—can be very harmful. It disrupts the work and family lives of those detained, harms their health, interferes with their defense, and imposes pressure on them to forego their trial rights and accede to the government’s charges in an effort to abbreviate time behind bars. For people with disabilities, however, pretrial incarceration is often even worse; it can utterly destabilize their physical and mental health and devastate their ability to participate in their proceedings. Set aside whether that would be a justifiable imposition if pretrial incarceration were truly necessary for the criminal or immigration systems to process their cases or if it truly served public safety. We demonstrate in this article that existing antidiscrimination law demands alternatives to pretrial incarceration, when it is demonstrably unnecessary and undermines the equal access of people with disabilities to the criminal or immigration processes that purport to justify it. The argument is somewhat novel but founded firmly on existing law: the Americans with Disabilities Act (ADA) and the Rehabilitation Act of 1973, their regulations, and well-developed interpretive case law
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Understanding the Relationship Between People and Their Environments Using Smartphone Data: A Study of Personality, Places Visited, and Emotional Experiences
Much has been theorized about the relationship between people and their environments. Certain people may be more inclined to visit certain types of places (e.g., campus, pub) and display different patterns of mobility as they move among them (e.g., number of places visited, distances traveled). Moreover, even the same place may affect people differently, depending on their psychological characteristics (e.g., personality). In this dissertation, I draw upon recent technological advances in smartphone-sensing methods to investigate the relationship between people’s psychological characteristics and their physical movements through space.
I begin by reviewing the existing psychological literature. I next describe features that can be extracted from GPS data and categorize them to provide a framework for collecting, analyzing, and discussing mobility. Then, I conduct an empirical investigation demonstrating this methodology at work. One-hundred and eighteen participants provided ecological momentary assessments, reporting their places visited and emotional states (e.g., feeling stressed, relaxed, sad) four times per day for two to four weeks. In addition to these ecological momentary assessments, place and mobility data were also automatically collected for forty students using their smartphone’s GPS sensors. I supplemented these data by collecting place attributions from an independent sample of 267 participants who evaluated the situational characteristics (e.g., sociality, positivity) of the most commonly visited locations. Lastly, I look at how people perceive places and whether their judgments about a location (e.g., predictions about the personality of those most likely to visit a location) demonstrate consensus or accuracy. A lens model analysis highlights the cues underlying these perceptions.
The results show how places visited (based on self-reported places) and mobility patterns (based on sensed GPS data) are related to people’s in-the-moment emotional experiences and their enduring psychological characteristics, such as their personality and wellbeing. I also examine how one’s personality interacts with the situational characteristics of a place to affect emotional states. For instance, one key finding reveals that, in general, participants experienced more positive emotions in social places (e.g., common rooms, pubs) but that this was especially true for more extraverted individuals. Lastly, I find that though people demonstrate consensus in their judgments when virtually visiting a place, they do not show significant accuracy.
My discussion focuses on the benefits of using place and GPS-based mobility measures to understand the relationship between people and their environments, as well as the unique methodological and logistical challenges inherent to this. I conclude by discussing potential implications for privacy and research ethics and point to promising directions for future research