Automatic mental health identification method based on natural gait pattern

Doshi

High Blood Press Cardiovasc Prev

et al. 2023

Introduction Depression and anxiety are common leading causes of disability and are associated with systemic effects including cardiovascular comorbidities. Low-income populations may experience higher frequencies of depressive or anxiety-related symptoms, and be at greater risk for developing hypertension. Aim We performed a cross-sectional study of low-income participants who completed hypertension and disability questionnaires as part of the 2017–2018 cycle of the National Health and Nutrition Examination Survey (NHANES) to identify associations between depressive/anxiety-related symptoms and hypertension status. Methods Multivariable logistic regressions were performed to identify whether (1) frequency of depressive symptoms, (2) frequency of anxiety-related symptoms, (3) self-reported depression medication use, or (4) self-reported anxiety medication use predicted previous hypertension diagnosis. Results A total of 74,285,160 individuals were represented in our cohort. Participants that reported taking depression (OR 2.72; 95% CI 1.41–5.24; P = 0.009) and anxiety (OR 2.50; 95% CI 1.42–4.41; P = 0.006) medications had greater odds of hypertension. Individuals with depressive feelings daily, monthly, and few times per year were more likely to have hypertension. Respondents with daily (OR 2.28; 95% CI 1.22–4.24; P = 0.021) and weekly (OR 1.88; 95% CI 1.05–3.38; P = 0.040) anxiety symptoms were more likely to have hypertension. Conclusions Low-income adults in the United States with symptoms of anxiety or depression have higher likelihood of hypertension than those with no symptoms. Respondents who indicated taking medication for anxiety disorders or depression were more likely to have been diagnosed with hypertension.

Section: Introductionmentioning

confidence: 99%

Impacts of Anxiety and Depression on Clinical Hypertension in Low-Income US Adults

Doshi

High Blood Press Cardiovasc Prev

et al. 2023

“…The ML algorithm prediction accuracy was 86% for depression and 78% for anxiety. Depressed and anxious individuals were found to have greater walking-movement variance [ 34 ].…”

Section: Laboratory-based Assessmentsmentioning

confidence: 99%

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

Richter

Fishbain

Richter‐Levin

et al. 2021

JPM

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

IEEE Trans. Neural Syst. Rehabil. Eng.

“…The internal physiological signals mainly include heart rate [14] [15], EEG [16] [17], galvanic skin [18] [19], nuclear magnetic [20] [21] and other signals. The external physiological signals mainly include eye movement [22] [23], gait [24] [25], speech [26] [27] and facial expression [28] [29], etc. The acquisition of these physiological signals above requires a lot of human and material resources and is not easy to promote.…”

Section: Introductionmentioning

confidence: 99%

TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection

Zhou

Liu

Shangguan

et al. 2023

In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people's lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog. We propose a time-aware attentionbased multimodal fusion depression detection network (TAMFN) to mine and fuse the multimodal features fully. The TAMFN model is constructed by a temporal convolutional network with the global information (GTCN), an intermodal feature extraction (IFE) module, and a time-aware attention multimodal fusion (TAMF) module. The GTCN model captures more temporal behavior information by combining local and global temporal information. The IFE module extracts the early interaction information between modalities to enrich the feature representation. The TAMF module guides the multimodal feature fusion by mining the temporal importance between different modalities. Our experiments are carried out on D-Vlog dataset, and the comparative experimental results report that our proposed TAMFN outperforms all benchmark models, indicating the effectiveness of the proposed TAMFN model.