Automated screening for distress: A perspective for the future

Rana, Rajib; Latif, Siddique; Gururajan, Raj; Gray, Anthony; Mackenzie, Geraldine; Humphris, Gerry; Dunn, Jeff

doi:10.1111/ecc.13033

Cited by 43 publications

(20 citation statements)

References 149 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There is work performing diagnosis with respiratory and lung sound analysis [170], which can work even with lowcost smartphones [171]. High mortality risk groups, including the elderly, can also be continuously monitored using speech analysis [172]. The patterns of coughs [173], [174], sneezing [173], throat clearing and swallowing sounds [175] can all be analysed using speech and sound processing.…”

Section: Voice Sound Data Analysismentioning

confidence: 99%

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

<div>COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.</div><div>As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.</div>

show abstract

Section: Voice Sound Data Analysismentioning

confidence: 99%

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is work performing diagnosis with respiratory and lung sound analysis [156], which can work even with lowcost smartphones [157]. High mortality risk groups, including the elderly, can also be continuously monitored using speech analysis [158]. The patterns of coughs [159], [160] and sneezing [159], throat clearing and swallowing sounds [161] can all be analysed using speech and sound processing.…”

Section: Voice Sound Data Analysismentioning

confidence: 99%

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is convergent psychological evidence [6], [7], [8], [9], [10], [11], [12], [13] that depression is marked by nonverbal objective cues related to head movements, facial expressions and gaze [14], [15], which can be automatically detected and analyzed without the intervention of clinicians. Building an automatic system based on such cues would not only provide an objective and repeatable evaluation but would also help alleviate key problems around cost and time requirements [16].…”

Section: Introductionmentioning

confidence: 99%

Spectral Representation of Behaviour Primitives for Depression Analysis

Song

Jaiswal

Shen

et al. 2022

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

Depression is a serious mental disorder affecting millions of people all over the world. Traditional clinical diagnosis methods are subjective, complicated and require extensive participation of clinicians. Recent advances in automatic depression analysis systems promise a future where these shortcomings are addressed by objective, repeatable, and readily available diagnostic tools to aid health professionals in their work. Yet there remain a number of barriers to the development of such tools. One barrier is that existing automatic depression analysis algorithms base their predictions on very brief sequential segments, sometimes as little as one frame. Another barrier is that existing methods do not take into account what the context of the measured behaviour is. In this paper, we extract multi-scale video-level features for video-based automatic depression analysis. We propose to use automatically detected human behaviour primitives as the low-dimensional descriptor for each frame. We also propose two novel spectral representations, i.e. spectral heatmaps and spectral vectors, to represent video-level multi-scale temporal dynamics of expressive behaviour. Constructed spectral representations are fed to Convolution Neural Networks (CNNs) and Artificial Neural Networks (ANNs) for depression analysis. We conducted experiments on the AVEC 2013 and AVEC 2014 benchmark datasets to investigate the influence of interview tasks on depression analysis. In addition to achieving state of the art accuracy in severity of depression estimation, we show that the task conducted by the user matters, that fusion of a combination of tasks reaches highest accuracy, and that longer tasks are more informative than shorter tasks, up to a point.

show abstract

Automated screening for distress: A perspective for the future

Cited by 43 publications

References 149 publications

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Spectral Representation of Behaviour Primitives for Depression Analysis

Contact Info

Product

Resources

About