2 44 CHAPTER 2 To make the information even more personal and relevant, a promising direction for future work is to include a prediction of the actual number of steps at the end of the day. Adding more (and personalized) information might strengthen the effectiveness of the system. To do so, we could apply a similar procedure to the one presented in this study, but instead replace our classification algorithms with regression machine learning algorithms. The predicted number of steps could be a valuable extension in addition to the currently implemented classification of the step goal. To conclude, machine learning is a viable asset to automate personalized daily physical activity prediction. Coaching can provide accurate and timely information on the participants’ physical activity, even early in the day. This is the result of applying machine learning to the behaviour of the individual participant as precisely and frequently measured by wearable sensors. The prediction of the participant meeting his goal in combination with the probability of such achievement allows for early intervention and can be used to provide support for personalized coaching. Also, the motivation for self-coaching might be increased, while every model is personalized, and the results are better fitted to the situation. Furthermore, machine learning techniques empower automated coaching and personalization. Acknowledgments We thank the Hanze University Health Program for providing the physical activity data of the Health Program and all the participants in the experiment.
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