Virtual coach: predict physical activity using a machine learning approach

One of the main causes of numerous health problems
is a lack of physical activity. To promote a more active lifestyle,
the Hanze University started a health promotion program. Participants were motivated to reach their daily goal of physical activity
by means of an activity tracker in combination with two-weekly
coaching sessions. Employing the data of the experiment, we
investigated the manners in which the predictability of physical
activity of a participant during the day can be improved. The
collected step count data was used to construct personalised
machine learning models, by taking into account the difference
between physical activities during weekdays on the one hand
and weekends on the other hand. The training of algorithms
per participant in combination with the time-slices weekdays,
weekend and the whole week improves the accuracy of the
prediction model. The performance of the models improves
even further when the individualised time-sliced models are
combined. More contextual data, like free time and working
hours, might even extend the accuracy. The use of personalised
prediction models, based on machine learning and time slices,
could become an addition in preventive personalized eHealth
systems and mobile activity monitoring. For instance, this can
constitute as a viable addition to a virtual coaching system to help
the participants to reach their daily goal. As the individualised
models allow for predictions of the progression of the physical
activity during the day, they enable the virtual coaching system
to intervene at the appropriate moment in time.