Using Digital Twins for AI enabled Virtual Coaching

Nowadays, one of the major current health risks is excessive sitting during work hours. Furthermore, the coronavirus disease 2019 (COVID-19) pandemic and the corresponding government state of emergency forced many people to work from home. These constraints carried out an important change in the lifestyle of people; for instance, the proportion of sitting time in front of a computer during working hours has increased considerably worldwide, particularly through the implementation of teleworking.
In order to motivate people to lead a less sedentary life, the Hanze University of Applied Sciences Groningen developed an automated recommender system. We investigated the possibility of automated coaching in order to increase physical activity and help people to reach their daily step goal. By monitoring people’s activity level and progress during the day, we predict personalized recommendations. The effect of these recommendations on the individual’s activity level forms the basis for a personalized coaching approach.
Step count data is used to train a machine learning algorithm that estimates the hourly probability of the individual achieving the daily steps goal. The outcome of this prediction is combined with the effect of the type recommendation for the individual to deliver the best recommendation for the individual. To show the practical usefulness, we constructed a platform to manage the data, rules, machine learning algorithms and clustering of participants. Results of initial pilots using the platform and app have given insight in the performance of and challenges associated with algorithm selection and personal model generation for the coaching package caused by the nature of the data. Further research will therefore be done in optimizing machine learning algorithms and tuning for human datasets.