Physical activity integration in blood glucose level prediction: different levels of data fusion

Blood glucose level (BGL) prediction contributes to more effective management of diabetes. Physical activity (PA), which affects BGL, is a crucial factor in diabetes management. Due to the erratic nature of PA's impact on BGL inter- and intra-patients, deploying PA in BGL prediction is challenging. Hence, it is crucial to discover optimal approaches for utilising PA to improve the performance of BGL prediction. This work contributes to this gap by proposing several PA-informed BGL prediction models. Different approaches are developed to extract information from PA data and integrate this information with BGL data at signal, feature, and decision levels. For signal-level fusion, different automatically-recorded PA data are fused with BGL data. Also, three feature engineering approaches are developed for feature-level fusion: subjective assessments of PA, objective assessments of PA, and statistics of PA. Furthermore, in decision-level fusion, ensemble learning is used to combine predictions from models trained with different inputs. Then, a comparative investigation is performed between the developed PA-informed approaches and the no-fusion approach, as well as between themselves. The analyses are performed on the publicly available Ohio dataset with rigorous evaluation. The results show that among the developed approaches, fusing heart rate data at the signal-level and PA intensity categories at the feature-level with BGL data are effective ways of deploying PA in BGL prediction.