Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models

Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models

Deep learning (DL) models are very useful for human activity recognition (HAR); these methods present better accuracy for HAR when compared to traditional, among other advantages. DL learns from unlabeled data and extracts features from raw data, as for the case of time-series acceleration. Sliding...

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Bibliographic Details
Main Authors: Milagros Jaén-Vargas, Karla Miriam Reyes Leiva, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo
Format: Article
Language:English
Published: PeerJ Inc. 2022-08-01
Series:PeerJ Computer Science
Subjects:
Accelerometer
Deep learning
Human activity recognition
Pattern recognition
Sliding windows
Motion capture
Online Access:https://peerj.com/articles/cs-1052.pdf

Internet

https://peerj.com/articles/cs-1052.pdf

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