activity
20172022
most citedPredicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

78 citations · 201 across the 12 of their papers we have counts for

collaborators

12 papers

cs.SD2022

Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression

Salvatore Fara, Stefano Goria, Emilia Molimpakis +1

Embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range o…

cs.SD2022★ 3 cited

Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities

Bahman Mirheidari, André Bittar, Nicholas Cummins +3

We present a novel feasibility study on the automatic recognition of Expressed Emotion (EE), a family environment concept based on caregivers speaking freely about their relative/f…

q-bio.QM2022★ 21 cited

Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +27

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of dai…

q-bio.QM2021

The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions

Shaoxiong Sun, Amos A Folarin, Yuezhou Zhang +25

Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute w…

stat.ML2021★ 78 cited

Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +21

The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals' proximity information, such as the nearby Bluet…

cs.HC2021★ 10 cited

Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder

Judith Dineley, Grace Lavelle, Daniel Leightley +22

The ease of in-the-wild speech recording using smartphones has sparked considerable interest in the combined application of speech, remote measurement technology (RMT) and advanced…