Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health

Author:

Ning Emma1ORCID,Cladek Andrea T.2ORCID,Ross Mindy K.3ORCID,Kabir Sarah2ORCID,Barve Amruta2ORCID,Kennelly Ellyn4ORCID,Hussain Faraz2ORCID,Duffecy Jennifer2ORCID,Langenecker Scott L.5ORCID,Nguyen Theresa3ORCID,Tulabandhula Theja6ORCID,Zulueta John2ORCID,Ajilore Olusola A.2ORCID,Demos Alexander P.1ORCID,Leow Alex7ORCID

Affiliation:

1. Psychology, University of Illinois Chicago, United States

2. Psychiatry, University of Illinois Chicago, United States

3. Biomedical Engineering, University of Illinois Chicago, United States

4. Psychology, Wayne State University, United States

5. Psychiatry, University of Utah, United States

6. Information and Decision Sciences, University of Illinois Chicago, United States

7. Psychiatry, Biomedical Engineering, University of Illinois Chicago, United States

Funder

National Institutes of Health

Publisher

ACM

Reference58 articles.

1. Cognitive rhythms

2. Hessa Alfalahi , Ahsan  H. Khandoker , Nayeefa Chowdhury , Dimitrios Iakovakis , Sofia  B. Dias , K.  Ray Chaudhuri , and Leontios  J. Hadjileontiadis . 2022. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Scientific Reports 2022 12:1 12, 1 (may 2022 ), 1–24. https://doi.org/10.1038/s41598-022-11865-7 10.1038/s41598-022-11865-7 Hessa Alfalahi, Ahsan H. Khandoker, Nayeefa Chowdhury, Dimitrios Iakovakis, Sofia B. Dias, K. Ray Chaudhuri, and Leontios J. Hadjileontiadis. 2022. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Scientific Reports 2022 12:1 12, 1 (may 2022), 1–24. https://doi.org/10.1038/s41598-022-11865-7

3. Sangwon Bae , Tammy Chung , Denzil Ferreira , Anind  K. Dey , and Brian Suffoletto . 2018. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addictive Behaviors 83 (aug 2018 ), 42–47. https://doi.org/10.1016/J.ADDBEH.2017.11.039 10.1016/J.ADDBEH.2017.11.039 Sangwon Bae, Tammy Chung, Denzil Ferreira, Anind K. Dey, and Brian Suffoletto. 2018. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addictive Behaviors 83 (aug 2018), 42–47. https://doi.org/10.1016/J.ADDBEH.2017.11.039

4. Luca Bedogni , Marco Di Felice, and Luciano Bononi . 2012 . By train or by car? Detecting the user’s motion type through smartphone sensors data. IFIP Wireless Days ( 2012). https://doi.org/10.1109/WD.2012.6402818 10.1109/WD.2012.6402818 Luca Bedogni, Marco Di Felice, and Luciano Bononi. 2012. By train or by car? Detecting the user’s motion type through smartphone sensors data. IFIP Wireless Days (2012). https://doi.org/10.1109/WD.2012.6402818

5. Retrospective recall of affect in clinically depressed individuals and controls

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