Anxiety Detection Leveraging Mobile Passive Sensing
| Autor: | Tara S. Peris, Lionel M. Levine, Kimmo Kärkkäinen, Migyeong Gwak, Majid Sarrafzadeh, Shayan Fazeli, Bita Zadeh, Alexander S. Young |
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| Rok vydání: | 2020 |
| Předmět: |
Class (computer programming)
Data collection Depression levels Computer science 05 social sciences Mental health Passive sensing 030227 psychiatry 03 medical and health sciences 0302 clinical medicine Human–computer interaction medicine Anxiety 0501 psychology and cognitive sciences medicine.symptom Disease management (health) 050104 developmental & child psychology |
| Zdroj: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783030649906 BODYNETS |
| DOI: | 10.1007/978-3-030-64991-3_15 |
| Popis: | Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and comparatively limited research has been applied to addressing the unique challenges around anxiety. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods, allowing for real-time mental health surveillance and disease management. This paper presents eWellness, an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual’s device in a continuous and passive manner. We report on an initial pilot study tracking ten people over the course of a month that showed a nearly 76% success rate at predicting daily anxiety and depression levels based solely on the passively monitored features. |
| Databáze: | OpenAIRE |
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