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Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms

Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms

Lakshmi Prayaga, Krishna Devulapalli, Chandra Prayaga
Copyright: © 2019 |Volume: 4 |Issue: 1 |Pages: 15
ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781522568605|DOI: 10.4018/IJBDAH.2019010103
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MLA

Prayaga, Lakshmi, et al. "Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms." IJBDAH vol.4, no.1 2019: pp.32-46. http://doi.org/10.4018/IJBDAH.2019010103

APA

Prayaga, L., Devulapalli, K., & Prayaga, C. (2019). Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 4(1), 32-46. http://doi.org/10.4018/IJBDAH.2019010103

Chicago

Prayaga, Lakshmi, Krishna Devulapalli, and Chandra Prayaga. "Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 4, no.1: 32-46. http://doi.org/10.4018/IJBDAH.2019010103

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Abstract

Wearable devices are contributing heavily towards the proliferation of data and creating a rich minefield for data analytics. Recent trends in the design of wearable devices include several embedded sensors which also provide useful data for many applications. This research presents results obtained from studying human-activity related data, collected from wearable devices. The activities considered for this study were working at the computer, standing and walking, standing, walking, walking up and down the stairs, and talking while walking. The research entails the use of a portion of the data to train machine learning algorithms and build a model. The rest of the data is used as test data for predicting the activity of an individual. Details of data collection, processing, and presentation are also discussed. After studying the literature and the data sets, a Random Forest machine learning algorithm was determined to be best applicable algorithm for analyzing data from wearable devices. The software used in this research includes the R statistical package and the SensorLog app.