204 citations · 382 across the 11 of their papers we have counts for
12 papers
Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity
Anastasiia Tokareva, Judith Dineley, Zoe Firth +19
Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research to date has largely used non-clinical, c…
A methodological framework and exemplar protocol for the collection and analysis of repeated speech samples
Nicholas Cummins, Lauren L. White, Zahia Rahman +8
Speech and language biomarkers have the potential to be regular, objective assessments of symptom severity in several health conditions, both in-clinic and remotely using mobile de…
Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study
Yuezhou Zhang, Amos A. Folarin, Yatharth Ranjan +20
Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the in…
Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +24
Objective: This study aimed to explore the associations between depression severity and wearable-measured circadian rhythms, accounting for seasonal impacts and quantifying seasona…
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Yuezhou Zhang, Amos A Folarin, Judith Dineley +25
Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processi…
Towards robust paralinguistic assessment for real-world mobile health (mHealth) monitoring: an initial study of reverberation effects on speech
Judith Dineley, Ewan Carr, Faith Matcham +4
Speech is promising as an objective, convenient tool to monitor health remotely over time using mobile devices. Numerous paralinguistic features have been demonstrated to contain s…
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
Shaoxiong Sun, Amos A. Folarin, Yuezhou Zhang +28
A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an accepta…
Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data
Salvatore Fara, Orlaith Hickey, Alexandra Georgescu +3
Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means tha…
Detecting the Severity of Major Depressive Disorder from Speech: A Novel HARD-Training Methodology
Edward L. Campbell, Judith Dineley, Pauline Conde +7
Major Depressive Disorder (MDD) is a common worldwide mental health issue with high associated socioeconomic costs. The prediction and automatic detection of MDD can, therefore, ma…
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
Shuo Liu, Jing Han, Estela Laporta Puyal +26
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data. The study utilise…
The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +19
Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography, is not suitable for long-term, continu…
Using smartphones and wearable devices to monitor behavioural changes during COVID-19
Shaoxiong Sun, Amos Folarin, Yatharth Ranjan +23
We aimed to explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to rapidly test the effect and response to NPIs aimed at limiti…