Cargando…
Multimodal Gait Abnormality Recognition Using a Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) Network Based on Multi-Sensor Data Fusion
Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable s...
Autores principales: | Li, Jing, Liang, Weisheng, Yin, Xiyan, Li, Jun, Guan, Weizheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675737/ https://www.ncbi.nlm.nih.gov/pubmed/38005489 http://dx.doi.org/10.3390/s23229101 |
Ejemplares similares
-
Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM
por: Zheng, Jinsong, et al.
Publicado: (2022) -
Network Intrusion Detection Method Combining CNN and BiLSTM in Cloud Computing Environment
por: Gao, Jing
Publicado: (2022) -
Human Activity Recognition Based on Residual Network and BiLSTM
por: Li, Yong, et al.
Publicado: (2022) -
Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM
por: Mahajan, Rhea, et al.
Publicado: (2021) -
ECG signal classification based on deep CNN and BiLSTM
por: Cheng, Jinyong, et al.
Publicado: (2021)