Next Article in Journal
Devising Mobile Sensing and Actuation Infrastructure with Drones
Next Article in Special Issue
Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone
Previous Article in Journal
A Novel Hard Decision Based Simultaneous Target Tracking and Classification Approach
Previous Article in Special Issue
Design and Evaluation of a Pervasive Coaching and Gamification Platform for Young Diabetes Patients
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(2), 623; https://doi.org/10.3390/s18020623

Personalized Physical Activity Coaching: A Machine Learning Approach

1
Johann Bernoulli Institute for Mathematics and Computer Science, Faculty of Science and Engineering (FSE), University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands
2
Institute of Communication, Hanze University of Applied Sciences, Media and ICT, Zernikeplein 11, 9746 AS Groningen, The Netherlands
3
Developmental Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands
4
School for Health Care Studies, Hanze University of Applied Sciences, Petrus Driessenstraat 3, 9714 CA Groningen, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 7 February 2018 / Revised: 15 February 2018 / Accepted: 15 February 2018 / Published: 19 February 2018
(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)
View Full-Text   |   Download PDF [2306 KB, uploaded 23 February 2018]   |  

Abstract

Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time. View Full-Text
Keywords: physical activity; machine learning; coaching; sedentary lifestyle physical activity; machine learning; coaching; sedentary lifestyle
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

Share & Cite This Article

MDPI and ACS Style

Dijkhuis, T.B.; Blaauw, F.J.; van Ittersum, M.W.; Velthuijsen, H.; Aiello, M. Personalized Physical Activity Coaching: A Machine Learning Approach. Sensors 2018, 18, 623.

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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top