Human ecology has played an essential role in the spread of mosquito-borne diseases. With standing water as a significant factor contributing to mosquito breeding, artificial containers disposed of as trash—which are capable of holding standing water—provide suitable environments for mosquito larvae to develop. The development of these larvae further contributes to the possibility for local transmission of mosquito-borne diseases in urban areas such as Zika virus. One potential solution to address this issue involves leveraging unmanned aerial vehicles that are already systematically becoming more utilized in the field of geospatial technology. With higher pixel resolution in comparison to satellite imagery, as well as having the ability to update spatial data more frequently, we are interested in investigating the feasibility of unmanned aerial vehicles as a potential technology for efficiently mapping potential breeding grounds. Therefore, we conducted a comparative study that evaluated the performance of an unmanned aerial vehicle for identifying artificial containers to that of conventionally utilized GPS receivers. The study was designed to better inform researchers on the current viability of such devices for locating a potential factor (i.e., small form factor artificial containers that can host mosquito breeding grounds) in the local transmission of mosquito-borne diseases. By assessing the performance of an unmanned aerial vehicle against ground-truth global position system technology, we can determine the effectiveness of unmanned aerial vehicles on this problem through our selected metrics of: timeliness, sensitivity, and specificity. For the study, we investigated these effectiveness metrics between the two technologies of interest in surveying a study area: unmanned aerial vehicles (i.e., DJI Phantom 3 Standard) and global position system-based receivers (i.e., Garmin GPSMAP 76Cx and the Garmin GPSMAP 78). We first conducted a design study with nine external participants, who collected 678 waypoint data and 214 aerial images from commercial GPS receivers and UAV, respectively. The participants then processed these data with professional mapping software for visually identifying and spatially marking artificial containers between the aerial imagery and the ground truth GPS data, respectively. From applying statistical methods (i.e., two-tailed, paired t-test) on the participants’ data for comparing how the two technologies performed against each other, our data analysis revealed that the GPS method performed better than the UAV method for the study task of identifying the target small form factor artificial containers.

The past several decades have seen significant advances in monitoring older adults' health and well-being. However, creating viable, practical monitoring systems for informing caregivers requires understanding which behaviors and signs to track and what approaches best present that information. To investigate how technology can be leveraged to better augment caregivers' workflows, we take a multi-stage, qualitative approach to gain insights into the needs of caregivers and the older adults receiving care. Specifically, we use a series of domain expert interviews, cognitive walkthroughs, and semi-structured interviews with residents, and we synthesize our takeaways using thematic analysis at each phase. Our results show that this type of monitoring technology has great potential to reduce the effort needed by caregivers to complete their responsibilities and communicate with their teams. Additionally, we found that older adults are receptive to the technology but their privacy and autonomy must be prioritized for the sake of their mental wellbeing. These insights will facilitate greater intelligent interface development for Person-Centered Care by identifying important design considerations and vital features that require system support.

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person’s ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world.

Increased virtual meeting software usage has allowed people to meet remotely in a more seamless fashion. However, compared to in-person meetings, valuable interaction cues such as impromptu group polling are less optimally executed due to increased difficulty in gauging remote participants, while also requiring prior meeting setup for automated counting with built-in polling tools. We propose a novel intelligent user interface approach for virtual meeting software that supports impromptu polling interactions by leveraging real-time hand gesture recognition and video filter feedback. We conducted studies to design and evaluate this intuitive gesture-based polling system with visual feedback. Our results demonstrated that our system was able to recognize attendees’ gestures and poll responses with reasonable accuracy, and showed improvements in hosts’ task workload performance. From our findings, our interface informs hosts of valuable results while maintaining organic gestural interaction cues with attendees similar to in-person meetings.