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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| Vydané v: | IEEE transactions on systems, man, and cybernetics. Systems Ročník 47; číslo 12; s. 3142 - 3149 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2168-2216, 2168-2232 |
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| Abstract | 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%. |
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| AbstractList | 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%. |
| Author | Wannenburg, Johan Malekian, Reza |
| Author_xml | – sequence: 1 givenname: Johan surname: Wannenburg fullname: Wannenburg, Johan email: johan.wannenburg@gmail.com organization: Dept. of Electr., Electron., & Comput. Eng., Univ. of Pretoria, Pretoria, South Africa – sequence: 2 givenname: Reza surname: Malekian fullname: Malekian, Reza email: reza.malekian@ieee.org organization: Dept. of Electr., Electron., & Comput. Eng., Univ. of Pretoria, Pretoria, South Africa |
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| SubjectTerms | Accelerometer Accelerometers Activity recognition Algorithms Classifiers Exercise Feature extraction Hidden Markov models Machine learning Machine learning algorithms Sensors Smart phones smartphone Smartphones |
| Title | Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing |
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