Automatic depression recognition by intelligent speech signal processing: A systematic survey
Depression has become one of the most common mental illnesses in the world. For better
prediction and diagnosis, methods of automatic depression recognition based on speech …
prediction and diagnosis, methods of automatic depression recognition based on speech …
[HTML][HTML] Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
Performing prescribed physical exercises during home-based rehabilitation programs plays
an important role in regaining muscle strength and improving balance for people with …
an important role in regaining muscle strength and improving balance for people with …
A multimodal fusion model with multi-level attention mechanism for depression detection
M Fang, S Peng, Y Liang, CC Hung, S Liu - Biomedical Signal Processing …, 2023 - Elsevier
Depression is a common mental illness that affects the physical and mental health of
hundreds of millions of people around the world. Therefore, designing an efficient and …
hundreds of millions of people around the world. Therefore, designing an efficient and …
Multimodal sensing for depression risk detection: integrating audio, video, and text data
Depression is a major psychological disorder with a growing impact worldwide. Traditional
methods for detecting the risk of depression, predominantly reliant on psychiatric …
methods for detecting the risk of depression, predominantly reliant on psychiatric …
Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A …
S Yasin, A Othmani, I Raza, SA Hussain - Computers in Biology and …, 2023 - Elsevier
Mental disorders are rapidly increasing each year and have become a major challenge
affecting the social and financial well-being of individuals. There is a need for phenotypic …
affecting the social and financial well-being of individuals. There is a need for phenotypic …
A novel EEG-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism
Z Zhang, Q Meng, LC **, H Wang, H Hou - Expert Systems with …, 2024 - Elsevier
Electroencephalography (EEG) is capable of capturing the evocative neural information
within the brain. As a result, it has been increasingly used for identifying neurological …
within the brain. As a result, it has been increasingly used for identifying neurological …
Multi-modal depression estimation based on sub-attentional fusion
Failure to timely diagnose and effectively treat depression leads to over 280 million people
suffering from this psychological disorder worldwide. The information cues of depression …
suffering from this psychological disorder worldwide. The information cues of depression …
[HTML][HTML] Speechformer-ctc: Sequential modeling of depression detection with speech temporal classification
Speech-based automatic depression detection systems have been extensively explored
over the past few years. Typically, each speaker is assigned a single label (Depressive or …
over the past few years. Typically, each speaker is assigned a single label (Depressive or …
Enhanced depression detection from speech using quantum whale optimization algorithm for feature selection
There is an urgent need to detect depression using a non-intrusive approach that is reliable
and accurate. In this paper, a simple and efficient unimodal depression detection approach …
and accurate. In this paper, a simple and efficient unimodal depression detection approach …
Attention guided learnable time-domain filterbanks for speech depression detection
Depression, as a global mental health problem, is lacking effective screening methods that
can help with early detection and treatment. This paper aims to facilitate the large-scale …
can help with early detection and treatment. This paper aims to facilitate the large-scale …