Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing

Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the...

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Vydáno v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 47; číslo 12; s. 3142 - 3149
Hlavní autoři: Wannenburg, Johan, Malekian, Reza
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accelerometer
Accelerometers
Activity recognition
Algorithms
Classifiers
Exercise
Feature extraction
Hidden Markov models
Machine learning
Machine learning algorithms
Sensors
Smart phones
smartphone
Smartphones
ISSN:2168-2216, 2168-2232
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Shrnutí:Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2016.2562509