skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System
Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders’ health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.  more » « less
Award ID(s):
1828010
PAR ID:
10344136
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Sensors
Volume:
21
Issue:
23
ISSN:
1424-8220
Page Range / eLocation ID:
7864
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents? The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers. 
    more » « less
  2. Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents? The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers. 
    more » « less
  3. Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents? The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers. 
    more » « less
  4. Online mental health support communities, in which volunteer counselors provide accessible mental and emotional health support, have grown in recent years. Despite millions of people using these platforms, the clinical effectiveness of these communities on mental health symptoms remains unknown. Although volunteers receive some training on the therapeutic skills proven effective in face-to-face environments, such as active listening and motivational interviewing, it is unclear how the usage of these skills in an online context affects people's mental health. In our work, we collaborate with one of the largest online peer support platforms and use both natural language processing and machine learning techniques to examine how one-on-one support chats on the platform affect clients' depression and anxiety symptoms. We measure how characteristics of support-providers, such as their experience on the platform and use of therapeutic skills (e.g. affirmation, showing empathy), affect support-seekers' mental health changes. Based on a propensity-score matching analysis to approximate a random-assignment experiment, results shows that online peer support chats improve both depression and anxiety symptoms with a statistically significant but relatively small effect size. Additionally, support providers' techniques such as emphasizing the autonomy of the client lead to better mental health outcomes. However, we also found that the use of some behaviors, such as persuading and providing information, are associated with worsening of mental health symptoms. Our work provides key understanding for mental health care in the online setting and designing training systems for online support providers. 
    more » « less
  5. This study aimed to examine changes in depression and anxiety symptoms from before to during the first 6 months of the COVID‐19 pandemic in a sample of 1,339 adolescents (9–18 years old, 59% female) from three countries. We also examined if age, race/ethnicity, disease burden, or strictness of government restrictions moderated change in symptoms. Data from 12 longitudinal studies (10 U.S., 1 Netherlands, 1 Peru) were combined. Linear mixed effect models showed that depression, but not anxiety, symptoms increased significantly (median increase = 28%). The most negative mental health impacts were reported by multiracial adolescents and those under ‘lockdown’ restrictions. Policy makers need to consider these impacts by investing in ways to support adolescents’ mental health during the pandemic. 
    more » « less