Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognit...

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Bibliographic Details
Published in:JMIR mHealth and uHealth 2019-02, Vol.7 (2), p.e11201
Main Authors: Li, Kenan, Habre, Rima, Deng, Huiyu, Urman, Robert, Morrison, John, Gilliland, Frank D, Ambite, José Luis, Stripelis, Dimitris, Chiang, Yao-Yi, Lin, Yijun, Bui, Alex AT, King, Christine, Hosseini, Anahita, Vliet, Eleanne Van, Sarrafzadeh, Majid, Eckel, Sandrah P
Format: Article
Language:English
Subjects:
Accelerometry - methods
Adult
Algorithms
Asthma
Datasets
Exercise
Experiments
Female
Human Activities - psychology
Human Activities - statistics & numerical data
Humans
Hypothesis testing
Machine Learning - standards
Machine Learning - statistics & numerical data
Male
Middle Aged
Multimedia
Multivariate Analysis
Original Paper
Pediatrics
Pollutants
Recognition, Psychology
Sensors
Smartphones
Time Factors
Time series
Wearable computers
Wearable Electronic Devices - psychology
Wearable Electronic Devices - standards
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