Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients
By Agnes Grünerbl, Amir Muaremi, Venet Osmani, Gernot Bahle, Stefan Ohler, Gerhard Tröster, Oscar Mayora-Ibarra, Christian Haring, Paul Lukowicz. IEEE journal of biomedical and health informatics, 2015
341 citations recorded by the source corpus.
Subfields: Applied Psychology, Experimental and Cognitive Psychology, Psychiatry and Mental health
View Paper: https://www.semanticscholar.org/paper/13df17d686ca0c9d6e36d56ac32165e388b99873
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In Applied Psychology
In Experimental and Cognitive Psychology
In Psychiatry and Mental health
Citations in the corpus
- Systematic review of smartphone-based passive sensing for health and wellbeing (2017, methodology): “Studies of bipolar disorder primarily used clinician assessments based on a battery of scales [34,44,45,53], although one used a self-report questionnaire [29].”
- Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, methodology): “By fusing phone call features with paralinguistic information, a state-recognition accuracy of 76%, as well as a precision and recall accuracy of, respectively, 97% and 97% for state-change detection, were achieved (Gr¨unerbl et al. 2015).”
- Smartphone Sensors for Health Monitoring and Diagnosis (2019, methodology): “A detailed review of smartphone-based fall detection systems was presented in References [161,162].”
- From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices (2016, methodology): “[108] report fusing data from acoustic, accelerometer and GPS sensors.”