An individual’s health trajectory is most influenced by personal lifestyle choices made regularly and frequently. It is now possible to measure, store, and analyze both multimodal lifestyle signals as well as multimodal physiological and behavioral health signals perpetually. Moreover, it is possible to model the effects of lifestyle precisely on health signals by collecting a variety of longitudinal data. These actions provide the inputs that affect changes in the health state of an individual based on their lifestyle decisions. With the advent of modern relatively inexpensive and common multi-modal data streams and sensing technologies, individuals now have large amounts of data and information about themselves that have the potential to transform decision-making in day-to-day life for health improvement. Multimodal analytics and contextual prediction and retrieval allow this data to make the best decisions to keep the health state optimal while making good lifestyle choices. This critical problem requires making a large variety of data constantly useful, contextually relevant, and most importantly useful in personalized decision-making.

Multimodal data streams are essential for analyzing personal life, environmental conditions, and social situations. Since these data streams have different granularities and semantics, the semantic gap becomes even more formidable. To make sense of all the multimodal correlated streams we must first synchronize them in the context of the application, and then analyze them to extract meaningful information. In this paper, we consider the problem of modeling an individual by using daily activity in order to understand their health and behavior. The first step is to correlate diverse data streams with atomic-interval, and segment a person's day into her daily activities. We collect the diverse data streams from the person's smartphone to classify every atomic-interval into a daily activity. Next, we use an interval growing technique for determining daily-activity-intervals and their attributes. Then, these daily-activity-intervals are labeled as the daily activities by using Bagging Formal Concept Analysis (BFCA). Finally, we build a personal chronicle, which is a person's time-ordered list of daily activities. This personal chronicle can then be used to model the person using learning techniques applied to daily activities in the chronicle and relating them to biomedical or behavioral signals. We present the results for this daily activity segmentation and recognition by using lifelogs of 23 participants.

People can now receive custom-made information through smartphones, tablets or wearable devices. However, people often tend to miss vital information, even reminders, in the flood of notifications. The problem of finding convenient moments for need-to-know information should be investigated. Because each person's message awareness pattern on a smart medium might be different, the necessity of personalized notification time should be emphasized. We believe that tracking changes in a user's physical activity and other contextual factors will reveal the most convenient moments. We propose a mobile framework, smartNoti, to carefully examine the user environment. The main contributions of our framework are: 1) developing an architecture to provide crucial information in a timely manner at a recognizable moment; 2) integrating, processing, training, and storing personalized latent features from heterogeneous data streams; 3) detecting user context transitions that might provide recognizable and available moments; and 4) predicting these moment and providing a notification message. The experimental validation on Intelligent Callback Reminder, which we implemented on an android application to notify a user missed or rejected call, demonstrates that our approach is effective. We believe that our findings can lead to intelligent strategies to issue unobtrusive notifications on today's smart phones at no extra cost, by using sensors and contextual factors.