scholarly journals Human Activity Recognition using Machine Learning Approach

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Abdul Lateef Haroon P S ◽  
Premachand D. R Premachand D. R
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Approach ◽  
Machine Learning Approach

Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset

2021 ◽  
Vol 191 ◽  
pp. 367-372
Author(s):  
Ariza-Colpas Paola ◽  
Oñate-Bowen Alvaro Agustín ◽  
Suarez-Brieva Eydy del Carmen ◽  
Oviedo-Carrascal Ana ◽  
Urina Triana Miguel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Human Activity Recognition Using Binary Sensors, BLE Beacons, an Intelligent Floor and Acceleration Data: A Machine Learning Approach

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1265 ◽  
Author(s):  
Jesús D. Cerón ◽  
Diego M. López ◽  
Bjoern M. Eskofier
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Class Imbalance ◽  
Human Activity Recognition ◽  
Training Dataset ◽  
Desktop Application ◽  
Acceleration Sensors ◽  
Machine Learning Approach ◽  
Mining Projects

Although there have been many studies aimed at the field of Human Activity Recognition, the relationship between what we do and where we do it has been little explored in this field. The objective of this paper is to propose an approach based on machine learning to address the challenge of the 1st UCAmI cup, which is the recognition of 24 activities of daily living using a dataset that allows to explore the aforementioned relationship, since it contains data collected from four data sources: binary sensors, an intelligent floor, proximity and acceleration sensors. The methodology for data mining projects CRISP-DM was followed in this work. To perform synchronization and classification tasks a java desktop application was developed. As a result, the accuracy achieved in the classification of the 24 activities using 10-fold-cross-validation on the training dataset was 92.1%, but an accuracy of 60.1% was obtained on the test dataset. The low accuracy of the classification might be caused by the class imbalance of the training dataset; therefore, more labeled data are necessary for training the algorithm. Although we could not obtain an optimal result, it is possible to iterate in the methodology to look for a way to improve the obtained results.

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