New machine learning approaches for real-life human activity recognition using smartphone sensor-based data
![Thumbnail](/dspace/bitstream/handle/2183/32779/GarciaGonzalez_Daniel_2023_New_machine_learning_approaches_real_life_human_activity.pdf.jpg?sequence=5&isAllowed=y)
Ver/Abrir
Use este enlace para citar
http://hdl.handle.net/2183/32779
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución 4.0 Internacional (CC BY 4.0)
Colecciones
- GI-RNASA - Artigos [193]
- GI-LBD - Artigos [49]
Metadatos
Mostrar el registro completo del ítemTítulo
New machine learning approaches for real-life human activity recognition using smartphone sensor-based dataFecha
2023Cita bibliográfica
D. Garcia-Gonzalez, D. Rivero, E. Fernandez-Blanco, & M.R. Luaces, "New machine learning approaches for real-life human activity recognition using smartphone sensor-based data", Knowledge-Based Systems, 262, 2023 [Online], DOI: doi:10.1016/j.knosys.2023.110260, Available: https://doi.org/10.1016/j.knosys.2023.110260
Resumen
[Abstract]: In recent years, mainly due to the application of smartphones in this area, research in human activity recognition (HAR) has shown a continuous and steady growth. Thanks to its wide range of sensors, its size, its ease of use, its low price and its applicability in many other fields, it is a highly attractive option for researchers. However, the vast majority of studies carried out so far focus on laboratory settings, outside of a real-life environment. In this work, unlike in other papers, progress was sought on the latter point. To do so, a dataset already published for this purpose was used. This dataset was collected using the sensors of the smartphones of different individuals in their daily life, with almost total freedom. To exploit these data, numerous experiments were carried out with various machine learning techniques and each of them with different hyperparameters. These experiments proved that, in this case, tree-based models, such as Random Forest, outperform the rest. The final result shows an enormous improvement in the accuracy of the best model found to date for this purpose, from 74.39% to 92.97%.
Palabras clave
HAR
Human activity recognition
Machine learning
Real life
Sensors
Smartphones
Human activity recognition
Machine learning
Real life
Sensors
Smartphones
Descripción
Financiado para publicar en acceso aberto. Universidade da Coruña/CISUG
Derechos
Atribución 4.0 Internacional (CC BY 4.0)