Cargando…
Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables †
Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart anno...
Autores principales: | Martindale, Christine F., Sprager, Sebastijan, Eskofier, Bjoern M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515106/ https://www.ncbi.nlm.nih.gov/pubmed/30995789 http://dx.doi.org/10.3390/s19081820 |
Ejemplares similares
-
Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models
por: Martindale, Christine F., et al.
Publicado: (2017) -
Robust Stride Segmentation of Inertial Signals Based on Local Cyclicity Estimation
por: Šprager, Sebastijan, et al.
Publicado: (2018) -
Inertial Sensor-Based Gait Recognition: A Review
por: Sprager, Sebastijan, et al.
Publicado: (2015) -
Specialized Hidden Markov Model Databases for Microbial Genomics
por: Gollery, Martin
Publicado: (2003) -
Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients
por: Roth, Nils, et al.
Publicado: (2021)