Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition
With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For tha...
Main Authors: | David Kreuzer, Michael Munz |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/3/789 |
Similar Items
-
Supervised ECG wave segmentation using convolutional LSTM
by: Aman Malali, et al.
Published: (2020-09-01) -
Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model
by: Jiayue Yang, et al.
Published: (2024-05-01) -
Deep Convolutional LSTM for improved flash flood prediction
by: Perry C. Oddo, et al.
Published: (2024-02-01) -
Prediction of Node Importance of Power System Based on ConvLSTM
by: Xu Wu, et al.
Published: (2022-05-01) -
Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches
by: Seyed Mahdi Mirhoseini Nejad, et al.
Published: (2023-01-01)