Nicholas Cummins

Lecturer

King’s College London

United Kingdom

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About

Nicholas Cummins is a Lecturer at King's College London, UK. His research focuses on the intersection of technology and mental health, particularly in the context of multiple sclerosis, eating disorders, and psychosis. Recent articles by Professor Cummins explore topics such as emotion detection from speech, the use of wearable devices for monitoring disability progression, and the analysis of mHealth data to predict depression symptoms.

Articles (14)

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2020

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2020

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2020

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2020

Collaborators (20)

Zulqarnain Rashid

King’s College London

UNITED KINGDOM

Nicholas Cummins

Research Interests

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Articles (14)

Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): A multi-centre prospective cohort study protocol

Automatic detection of expressed emotion from Five-Minute Speech Samples

Automated analysis of speech as a marker of sub-clinical psychotic experiences

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing

A wearable device perspective on the standard definitions of disability progression in multiple sclerosis

Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosis

Generalized two-stage rank regression framework for depression score prediction from speech

Automatic Assessment of Depression from Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders

I see it in your eyes: Training the shallowest-possible CNN to recognise emotions and pain from muted web-assisted in-the-wild video-chats in real-time

Real-time assessment of COVID-19 prevalence among multiple sclerosis patients: a multicenter European study

Collaborators (20)

Zulqarnain Rashid

Vedhas Pandit

Nora Trompeter

richard dobson

Tom Spencer

Amos A. Folarin

André Bittar

Sylvane Desrivières

Yuezhou Zhang

Sarah Morgan

Raquel Bailón

Melinda Magyari

Katie M White

Vidhyasaharan Sethu

Helen Fisher

Heidi Christensen

Gloria Dalla Costa

Linglong Qian

Julian Baudinet

Judith Dineley

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