WeMoD: A Machine Learning Approach for Wearable and Mobile Physical Activity Prediction

It is indisputable that physical activity (PA) is vital for an individual's health and well-being. However, globally, one in four adults do not meet the recommended levels of PA, with significant personal and socioeconomic implications. In recent years, a significant amount of work has explored the potential of pervasive computing and self-tracking for increasing PA. Adaptive and personalized goal-setting has proven to be one of the most efficient methods in this direction. To this end, we propose a Machine Learning (ML) approach, WeMoD, which can be used to predict a user's future daily step count for setting challenging yet achievable goals. For the development of WeMoD, we utilize heterogeneous, multimodal human data collected unobtrusively in the wild. Additionally, we use a novel fusion of physiological, behavioral, and contextual features, which according to the experimental results, has a positive effect on the predictive ability of the models. Specifically, we can predict a user's step count with a MAE of 1930 steps and further improve this performance through personalization with a MAE of 1908 steps, paving the way for future work in this field.