Welcome to the InfoSci Platform
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: © 2022 |Pages: 15
ISBN13: 9781668462911|ISBN10: 1668462915|EISBN13: 9781668462928
DOI: 10.4018/978-1-6684-6291-1.ch053
Cite Chapter Cite Chapter

MLA

Prayaga, Lakshmi, et al. "Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms." Research Anthology on Machine Learning Techniques, Methods, and Applications, edited by Information Resources Management Association, IGI Global, 2022, pp. 1023-1037. https://doi.org/10.4018/978-1-6684-6291-1.ch053

APA

Prayaga, L., Devulapalli, K., & Prayaga, C. (2022). Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms. In I. Management Association (Ed.), Research Anthology on Machine Learning Techniques, Methods, and Applications (pp. 1023-1037). IGI Global. https://doi.org/10.4018/978-1-6684-6291-1.ch053

Chicago

Prayaga, Lakshmi, Krishna Devulapalli, and Chandra Prayaga. "Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms." In Research Anthology on Machine Learning Techniques, Methods, and Applications, edited by Information Resources Management Association, 1023-1037. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6291-1.ch053

Export Reference

Mendeley
Favorite

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.