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Conference Paper
Challenge in Classification of Depressive Symptoms Using Actigraphy Data
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- Authors
- Sehwan Moon, Eunkyoung Jeon, Aram Lee, Min Jhon, Ju-Wan Kim, Jeong Eun Kim
- Issue Date
- 2023-12
- Citation
- International Conference on Bioinformatics and Biomedicine (BIBM) 2023, pp.4919-4921
- Language
- English
- Type
- Conference Paper
- Abstract
- Monitoring human behavior through wearable devices has potential in psychiatry. Among them, actigraphy data has been used to classify depression and detect depressive symptoms. We aim to collect a larger number of data to measure classification performance. This study evaluates the performance of classifying depressive symptoms solely on actigraphy data using both public (n=1549) and collected (n=3145) datasets. We found that there are challenges in classifying depressive symptoms from actigraphy data.
- KSP Keywords
- Classification Performance, Depressive symptoms, Wearable Devices, data monitoring, human behavior