Cargando…
Quantifying Digital Biomarkers for Well-Being: Stress, Anxiety, Positive and Negative Affect via Wearable Devices and Their Time-Based Predictions
Wearable devices have become ubiquitous, collecting rich temporal data that offers valuable insights into human activities, health monitoring, and behavior analysis. Leveraging these data, researchers have developed innovative approaches to classify and predict time-based patterns and events in huma...
Autores principales: | Saylam, Berrenur, İncel, Özlem Durmaz |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649682/ https://www.ncbi.nlm.nih.gov/pubmed/37960685 http://dx.doi.org/10.3390/s23218987 |
Ejemplares similares
-
Analysis of Movement, Orientation and Rotation-Based Sensing for Phone Placement Recognition
por: Durmaz Incel, Ozlem
Publicado: (2015) -
On the Use of a Convolutional Block Attention Module in Deep Learning-Based Human Activity Recognition with Motion Sensors
por: Agac, Sumeyye, et al.
Publicado: (2023) -
Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity
por: Kourtis, Lampros C., et al.
Publicado: (2019) -
Digital Biomarkers for Well-being Through Exergame Interactions: Exploratory Study
por: Petsani, Despoina, et al.
Publicado: (2022) -
Multimodal Wireless Sensor Network-Based Ambient Assisted Living in Real Homes with Multiple Residents
por: Tunca, Can, et al.
Publicado: (2014)