Personalized Physical Activity Coaching: A Machine Learning Approach

Sensors
Talko B DijkhuisMarco Aiello

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 c...Continue Reading

References

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Citations

May 31, 2018·Sensors·Oresti BanosHector Pomares
Nov 11, 2020·Sensors·Filippo PiccininiAntonella Carbonaro
Feb 17, 2021·Public Health Nutrition·Han Shi Jocelyn ChewYing Lau

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Software Mentioned

Scikit learn
Scikit
Anaconda
Jupyter Notebooks
Python
Heroku
Fitbit
Python Pickle
Azure
PostgreSQL

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