Next Article in Journal
Structuring Surfaces by Microfinishing Using Defined Abrasive Belts
Previous Article in Journal
Stochastic Kinematic Process Model with an Implemented Wear Model for High Feed Dry Grinding
Article Menu

Export Article

Open AccessArticle
Inventions 2017, 2(4), 32; doi:10.3390/inventions2040032

Activity-Aware Physiological Response Prediction Using Wearable Sensors

Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-7566, USA
*
Author to whom correspondence should be addressed.
Received: 1 November 2017 / Revised: 11 November 2017 / Accepted: 16 November 2017 / Published: 21 November 2017
(This article belongs to the Special Issue Frontiers in Wearable Devices)
View Full-Text   |   Download PDF [7464 KB, uploaded 21 November 2017]   |  

Abstract

Prediction of physiological responses can have a number of applications in the health and medical fields. However, this can be a challenging task due to interdependencies between these responses, physical activities, environmental factors and the individual’s mental state. In this work, we focus on forecasting physiological responses in dynamic scenarios where individuals are performing exercises and complex activities of daily life. We minimize the effect of environmental and physiological factors in order to focus on the effect of physical activities. In particular, we focus on forecasting heart rate and respiratory rate due to their relevance in medical and fitness training. We aim to forecast these physiological responses up to 60 s into the future, study the effect of different predictors that incorporate different sensing modalities and different amounts of historical data and analyze the performance of various strategies for prediction. Activity information is incorporated by clustering the data streams and fitting different predictive models per cluster. The effect of clustering is also studied by performing a hierarchical analysis on the clustering parameter, and we observe that activity clustering does improve the performance in our proposed methodology when predicting physiological response across modalities. View Full-Text
Keywords: [-15]time series analysis; physiological parameter forecasting; cluster analysis; multi-modal data [-15]time series analysis; physiological parameter forecasting; cluster analysis; multi-modal data
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lokare, N.; Zhong, B.; Lobaton, E. Activity-Aware Physiological Response Prediction Using Wearable Sensors. Inventions 2017, 2, 32.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Inventions EISSN 2411-5134 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top