Research in the area of internet-of-things, cyberphysical-systems, and smart health often employ sensor systems at residences for continuous monitoring. Such research-oriented residential monitoring systems (RRMSs) usually face two major challenges, long-term reliable operation management and validation of system functionality with minimal human effort. Targeting these two challenges, this paper describes a monitor of monitoring systems with ground-truth validation capabilities, M2G. It consists of two subsystems, the Monitor2system and the Ground-truth validation system. The Monitor2system encapsulates a flexible set of general-purpose components to monitor the operation and connectivity of heterogeneous sensor devices (e.g. smart watches, smart phones, microphones, beacons, etc.), a local base-station, as well as a cloud server. It provides a user-friendly interface and supports different types of RRMSs in various contexts. The system also features a ground truth validation system to support obtaining ground truth in the field. Additionally, customized alerts can be sent to remote administrators and other personnel to report any dysfunction or inaccuracy of the system in real time. M2G is applied to three very different case studies: the M2FED system which monitors family eating dynamics [1], an in-home wireless sensing system for monitoring nighttime agitation [2], and the BESI system which monitors behavioral and environmental parameters to predict health events and to provide interventions [3]. The results indicate that M2G is a comprehensive system that (i) requires small cost in time and effort to adapt to an existing RRMS, (ii) provides reliable data collection and reduction in data loss by detecting faults in real-time, and (iii) provides a convenient and timely ground truth validation facility.

The obesity epidemic is the primary cause of recent increases in heart disease, diabetes, cancer, and other diseases that place an untenable strain on healthcare and public health. One of the primary behavioral causes, i.e. dietary intake, is a behavior that science has had little success in understanding, much less affecting. Current behavioral science suggests that family eating dynamics (FED) have high potential to impact child and parent dietary intake and obesity rates. In contrast to traditional obesity related approaches that focus on dietary intakes (e.g., what and how much is being eaten), FED based approaches focus on family mealtime and home food environment (e.g., who is eating, when, where, with whom, interpersonal stress) for modeling behavior and providing feedback that has potential for successful behavior modification. This poster briefly presents M2FED, an integrated system for monitoring and modeling FED. The system is under development, and it consists of in-situ and wearable sensors, and smartphones that collect synchronized real-time FED data used to iteratively develop dynamic, contextualized FED models.