2 43 MACHINE LEARNING ENABLED PERSONALIZED PHYSICAL ACTIVITY COACHING As presented in the state of the art literature, the total number of steps differ significantly between Sunday and rest of the weekdays [5], [6]. Within this health promotion program, the focus was on improving physical activity during working hours and commuting. Therefore, the machine learning models were trained based on the normal workweek. Only one model per participant, based on the five weekdays, is adequate to predict whether or not a participant will meet his or her threshold. It may be necessary to conduct different models for the weekend and weekdays when a health promotion program is expanded to weekends. A reason to establish more than one or two models per participant is found in the variances between weekdays [5]. Examples of different factors that could influence the level of physical activity are weekly sport obligations, weekly meetings, or lunch walks on certain days. Constructing a model per weekday might establish an even more personalized and precise prediction. In the present work, we only train our machine learning algorithms on variables provided by the activity tracker, extending this set of variables with other (exogenous) variables from other data sources. For example, the data can be extended to include information on the changes in the weather conditions and/or season, which are known to correlate with the day-to-day activity [5], [53], or non-working time during weekdays like national holidays and free time, or part-time working schedule, for the activity level differs between non-occupational and occupational time, or the influence and effectiveness of coaching and interventions. Adding the mentioned factors to the dataset might improve the predictive accuracy of the model and might increase the effectiveness of the coaching process. To apply the personalized machine learning models effectively, they have to become a part of a larger ecosystem. An ideal coaching process is fully tailored to the individual participant. One of the most important characteristics of the personalization of a coaching strategy consists in the timing and ease to execute triggers to change behaviour [54]. To support these two aspects of coaching, timely information on the participant and the effectiveness of the coaching strategy are needed. Coaching might not be limited to a personal real-life coach but also may include virtual coaching. An example of a possible use of the system is: at the moment the participant doesn’t score a ‘yes’ for two hours in a row on the prediction of meeting his threshold, a notification is sent out to both the participant and the coach. On the basis of this notification, the participant and the coach can take action; the coach can timely intervene to stimulate his client to become physically active and the participant can become instantly more active. Blok et al. proposed a system which combines the real-time analysis of activity tracker data and other personal streaming data as well as the evaluation of virtual coaching strategies, which enables it to tune the coaching to the person [55]. The present work could serve as a central component of a virtual coach system like that envisioned by Blok et al. [55].
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