Predictive health behavior modeling using multimodal feature correlations via Medical Internet-of-Things devices

Due to the advent of IoT (Internet of Things) based devices that help to monitor different human behavioral aspects. These aspects include sleeping patterns, activity patterns, heart rate variability (HRV) patterns, location-based moving patterns, blood oxygen levels, etc. A correlative study of these patterns can be used to find linkages of behavioral patterns with human health conditions. To perform this task, a wide variety of models is proposed by researchers, but most of them vary in terms of used parameters, which limits their accuracy of analysis. Moreover, most of these models are highly complex and have lower parameter flexibility, thus, cannot be scaled for real-time use cases. To overcome these issues, this paper proposes design of a behavior modeling method that assists in future health predictions via multimodal feature correlations using medical IoT devices via deep transfer learning analysis. The proposed model initially collects large-scale sensor data about the subjects, and correlates them with the existing medical conditions. This correlation is done via extraction of multidomain feature sets that assist in spectral analysis, entropy evaluations, scaling estimation, and window-based analysis. These multidomain feature sets are selected by a Firefly Optimizer (FFO) and are used to train a Recurrent Neural Network (RNN) Model, that assists in prediction of different diseases. These predictions are used to train a recommendation engine that uses Apriori and Fuzzy C Means (FCM) for suggesting corrective behavioral measures for a healthier lifestyle under real-time conditions. Due to these operations, the proposed model is able to improve behavior prediction accuracy by 16.4%, precision of prediction by 8.3%, AUC (area under the curve) of prediction by 9.5%, and accuracy of corrective behavior recommendation by 3.9% when compared with existing methods under similar evaluation conditions.