Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis

H Alfalahi, AH Khandoker, N Chowdhury, D Iakovakis… - Scientific reports, 2022 - nature.com
The unmet timely diagnosis requirements, that take place years after substantial neural loss
and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers …

Systematic review of digital phenotyping and machine learning in psychosis spectrum illnesses

J Benoit, H Onyeaka, M Keshavan… - Harvard Review of …, 2020 - journals.lww.com
Background Digital phenotyping is the use of data from smartphones and wearables
collected in situ for capturing a digital expression of human behaviors. Digital phenotyping …

A review of detection techniques for depression and bipolar disorder

D Highland, G Zhou - Smart Health, 2022 - Elsevier
Depression and bipolar disorder are mood disorders affecting millions of people worldwide
that can have severe impacts on one's quality of life. Our ability to detect these illnesses is …

Affective state prediction from smartphone touch and sensor data in the wild

R Wampfler, S Klingler, B Solenthaler… - Proceedings of the …, 2022 - dl.acm.org
Knowledge of users' affective states can improve their interaction with smartphones by
providing more personalized experiences (eg, search results and news articles). We present …

Deep learning: A primer for psychologists.

CJ Urban, KM Gates - Psychological Methods, 2021 - psycnet.apa.org
Deep learning has revolutionized predictive modeling in topics such as computer vision and
natural language processing but is not commonly applied to psychological data. In an effort …

A review of emotion recognition methods from keystroke, mouse, and touchscreen dynamics

L Yang, SF Qin - Ieee Access, 2021 - ieeexplore.ieee.org
Emotion can be defined as a subject's organismic response to an external or internal
stimulus event. The responses could be reflected in pattern changes of the subject's facial …

[HTML][HTML] Predicting clinically relevant changes in bipolar disorder outside the clinic walls based on pervasive technology interactions via smartphone typing dynamics

CC Bennett, MK Ross, EG Baek, D Kim… - Pervasive and Mobile …, 2022 - Elsevier
Modeling smartphone keyboard dynamics as the foundation of an early warning system
(EWS) for mood instability holds potential to expand the reach of healthcare beyond the …

A novel approach to clustering accelerometer data for application in passive predictions of changes in depression severity

MK Ross, T Tulabandhula, CC Bennett, EG Baek… - Sensors, 2023 - mdpi.com
The treatment of mood disorders, which can become a lifelong process, varies widely in
efficacy between individuals. Most options to monitor mood rely on subjective self-reports …

Affective state prediction based on semi-supervised learning from smartphone touch data

R Wampfler, S Klingler, B Solenthaler… - Proceedings of the …, 2020 - dl.acm.org
Gaining awareness of the user's affective states enables smartphones to support enriched
interactions that are sensitive to the user's context. To accomplish this on smartphones, we …

Using ambulatory assessment to measure dynamic risk processes in affective disorders

JP Stange, EM Kleiman, RJ Mermelstein… - Journal of Affective …, 2019 - Elsevier
Background Rapid advances in the capability and affordability of digital technology have
begun to allow for the intensive monitoring of psychological and physiological processes …