Anxiety Detection Leveraging Mobile Passive Sensing
- URL: http://arxiv.org/abs/2008.03810v1
- Date: Sun, 9 Aug 2020 20:22:52 GMT
- Title: Anxiety Detection Leveraging Mobile Passive Sensing
- Authors: Lionel Levine, Migyeong Gwak, Kimmo Karkkainen, Shayan Fazeli, Bita
Zadeh, Tara Peris, Alexander Young, Majid Sarrafzadeh
- Abstract summary: Anxiety disorders are the most common class of psychiatric problems affecting both children and adults.
Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods.
eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
- Score: 53.11661460916551
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Anxiety disorders are the most common class of psychiatric problems affecting
both children and adults. However, tools to effectively monitor and manage
anxiety are lacking, and comparatively limited research has been applied to
addressing the unique challenges around anxiety. Leveraging passive and
unobtrusive data collection from smartphones could be a viable alternative to
classical methods, allowing for real-time mental health surveillance and
disease management. This paper presents eWellness, an experimental mobile
application designed to track a full-suite of sensor and user-log data off an
individual's device in a continuous and passive manner. We report on an initial
pilot study tracking ten people over the course of a month that showed a nearly
76% success rate at predicting daily anxiety and depression levels based solely
on the passively monitored features.
Related papers
- Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population.
Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app.
We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
arXiv Detail & Related papers (2024-09-24T16:05:17Z) - Objective Prediction of Tomorrow's Affect Using Multi-Modal
Physiological Data and Personal Chronicles: A Study of Monitoring College
Student Well-being in 2020 [0.0]
The goal of our study was to investigate the capacity to more accurately predict affect through a fully automatic and objective approach using multiple commercial devices.
Longitudinal physiological data and daily assessments of emotions were collected from a sample of college students using smart wearables and phones for over a year.
Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.
arXiv Detail & Related papers (2022-01-26T23:06:20Z) - Designing A Clinically Applicable Deep Recurrent Model to Identify
Neuropsychiatric Symptoms in People Living with Dementia Using In-Home
Monitoring Data [52.40058724040671]
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia.
Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions.
This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
arXiv Detail & Related papers (2021-10-19T11:45:01Z) - Learning Language and Multimodal Privacy-Preserving Markers of Mood from
Mobile Data [74.60507696087966]
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is daily smartphone usage.
We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
arXiv Detail & Related papers (2021-06-24T17:46:03Z) - Deep Multi-task Learning for Depression Detection and Prediction in
Longitudinal Data [50.02223091927777]
Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally.
Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment.
We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
arXiv Detail & Related papers (2020-12-05T05:14:14Z) - Multimodal Privacy-preserving Mood Prediction from Mobile Data: A
Preliminary Study [34.550824104906255]
Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is from daily smartphone usage.
We study behavioral markers or daily mood using a recent dataset of mobile behaviors from high-risk adolescent populations.
arXiv Detail & Related papers (2020-12-04T01:44:22Z) - Passive detection of behavioral shifts for suicide attempt prevention [0.0]
We present a non-invasive machine learning model to detect behavioral shifts in psychiatric patients from unobtrusive data collected by a smartphone app.
Our clinically validated results shed light on the idea of an early detection mobile tool for the task of suicide attempt prevention.
arXiv Detail & Related papers (2020-11-14T11:44:43Z) - Predicting Parkinson's Disease with Multimodal Irregularly Collected
Longitudinal Smartphone Data [75.23250968928578]
Parkinsons Disease is a neurological disorder and prevalent in elderly people.
Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests.
We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
arXiv Detail & Related papers (2020-09-25T01:50:15Z) - Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using
Deep Multiple-Instance Learning [59.74684475991192]
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old.
PD symptoms include tremor, rigidity and braykinesia.
We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
arXiv Detail & Related papers (2020-05-06T09:02:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.