Recent advances in contactless sensing technologies for mental health monitoring

M Nouman, SY Khoo, MAP Mahmud… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
The process of monitoring mental health has relied on methods, such as invasive sensing
and self-reporting. The use of these methods has been limited because of the invasiveness …

Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses

KY Huang, CH Wu, MH Su - Pattern Recognition, 2019 - Elsevier
Mood disorders, including unipolar depression (UD) and bipolar disorder (BD), have
become some of the commonest mental health disorders. The absence of diagnostic …

Cell-Coupled Long Short-Term Memory With -Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features

MH Su, CH Wu, KY Huang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In early stages, patients with bipolar disorder are often diagnosed as having unipolar
depression in mood disorder diagnosis. Because the long-term monitoring is limited by the …

Development of Sorrow Analysis Dataset for Speech Depression Prediction

MF Alghifari, TS Gunawan… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Computers can get insight into the user's mental state, including depression prediction, by
analyzing speech signals. Numerous uses exist, ranging from customer service to …

A framework for monitoring of depression patient using WBAN

J Saha, S Biswas, T Bhattacharyya… - … signal processing and …, 2016 - ieeexplore.ieee.org
Wireless Body Area Networks consist of low power lightweight wearable and/or implantable
sensor nodes that are often placed remotely for applications like ubiquitous health care …

On the optimum speech segment length for depression detection

MF Alghifari, TS Gunawan, MAW Nordin… - … conference on smart …, 2019 - ieeexplore.ieee.org
Depression is a worldwide problem, which according to the World Health Organization, is
the largest contributor to global disability. According to a study, around 18336 Malaysians …

A Survey: Feature Extraction Techniques and machine learning models for Depression Analysis

Y Nikam, A Nair, A Nikam, A Mhaske… - 2021 12th …, 2021 - ieeexplore.ieee.org
The most common psychiatric disorder is Clinical Depression. More than fifteen percent of
people undergo an incident of major depression sometime during their life. There is an …

[HTML][HTML] Who's in Charge? Information Technology and Disability Justice in the United States

A Gibson, R Williams - 2022 - just-tech.ssrc.org
Disabled people in the United States are surrounded, defined, and, to some degree,
controlled by data, technology, and information—from medical technology and therapies to …

[PDF][PDF] Smart model for Depression detection Using Deep Learning

P Kapse, VK Gargline - kalaharijournals.com
In this research paper to classifying Depression detection People datasets using a deep
neural network model, namely, Smart model for Depression detection (SMDD) is proposed …

[PDF][PDF] ARTIFICIAL INTELLIGENCE BASED DEPRESSION RECOGNITION SYSTEM

L Joy, N Alex, S Thomas, T Sunny, A George - 2020 - academia.edu
Prediction of depression manually is a time consuming process. Apps and chat boxes with
cognitive capabilities can recognize the mental state of a person. This project proposes a …