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Recognizing Human Activity in Free-Living Using Multiple Body-Worn Accelerometers.
- Source :
- IEEE Sensors Journal; 8/15/2017, Vol. 17 Issue 16, p5290-5297, 8p
- Publication Year :
- 2017
-
Abstract
- Recognizing human activity is very useful for an investigator about a patient’s behavior and can aid in prescribing activity in future recommendations. The use of body worn accelerometers has been demonstrated to be an accurate measure of human activity; however, research looking at the use of multiple body worn accelerometers in a free living environment to recognize a wide range of activities is not evident. This paper aimed to successfully recognize activity and sub-category activity types through the use of multiple body worn accelerometers in a free-living environment. Ten participants (Age = 23.1 ± 1.7 years, height =171.0 ± 4.7 cm, and mass = 78.2 ± 12.5 Kg) wore nine body-worn accelerometers for a day of free living. Activity type was identified through the use of a wearable camera, and subcategory activities were quantified through a combination of free-living and controlled testing. A variety of machine learning techniques consisting of preprocessing algorithms, feature, and classifier selections were tested, accuracy, and computing time were reported. A fine k-nearest neighbor classifier with mean and standard deviation features of unfiltered data reported a recognition accuracy of 97.6%. Controlled and free-living testing provided highly accurate recognition for sub-category activities (>95.0%). Decision tree classifiers and maximum features demonstrated to have the lowest computing time. Results show that recognition of activity and sub-category activity types is possible in a free-living environment through the use of multiple body worn accelerometers. This method can aid in prescribing recommendations for activity and sedentary periods for healthy living. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 1530437X
- Volume :
- 17
- Issue :
- 16
- Database :
- Complementary Index
- Journal :
- IEEE Sensors Journal
- Publication Type :
- Academic Journal
- Accession number :
- 124295823
- Full Text :
- https://doi.org/10.1109/JSEN.2017.2722105