Event-Based Motion Capture System for Online Multi-Quadrotor Localization and Tracking.

Motion capture systems are crucial in developing multi-quadrotor systems due to their ability to provide fast and accurate ground truth measurements for tracking and control. This paper presents the implementation details and experimental validation of a relatively low-cost motion-capture system for multi-quadrotor motion planning using an event camera. The real-time, multi-quadrotor detection and tracking tasks are performed using a deep learning network You-Only-Look-Once (YOLOv5) and a k-dimensional (k-d) tree, respectively. An optimization-based decentralized motion planning algorithm is implemented to demonstrate the effectiveness of this motion capture system. Extensive experimental evaluations were performed to (1) compare the performance of four deep-learning algorithms for high-speed multi-quadrotor detection on event-based data, (2) study precision, recall, and F1 scores as functions of lighting conditions and camera motion, and (3) investigate the scalability of this system as a function of the number of quadrotors flying in the arena. Comparative analysis of the deep learning algorithms on a consumer-grade GPU demonstrates a 4.8× to 12× sampling/inference rate advantage that YOLOv5 provides over representative one- and two-stage detectors and a 1.14× advantage over YOLOv4. In terms of precision and recall, YOLOv5 performed 15% to 18% and 27% to 41% better than representative state-of-the-art deep learning networks. Graceful detection and tracking performance degradation was observed in the face of progressively darker ambient light conditions. Despite severe camera motion, YOLOv5 precision and recall values of 94% and 98% were achieved, respectively. Finally, experiments involving up to six indoor quadrotors demonstrated the scalability of this approach. This paper also presents the first open-source event camera dataset in the literature, featuring over 10,000 fully annotated images of multiple quadrotors operating in indoor and outdoor environments.

OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses.

Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as "dietary lapses" can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.

The Wills Eye Glaucoma App: Interest of Patients and Their Caregivers in a Smartphone-based and Tablet-based Glaucoma Application.

PURPOSE: To evaluate the interest of glaucoma patients and their caregivers in a smartphone-based and tablet-based glaucoma application (App), developed by the Wills Eye Glaucoma Research Center in collaboration with Drexel University. MATERIALS AND METHODS: Cross-sectional survey of patients with glaucoma and their caregivers. Main outcome measures are answers to survey questions regarding how receptive participants are to using the Glaucoma App. RESULTS: Fifty subjects completed the survey. The mean age (SD) was 59.5 (±17.3) years. A total of 88.6% of the participants lived in a household with access to a smartphone or tablet. The majority (72.3%) of participants would consider downloading the Glaucoma App, and younger participants (<65 y) were more likely to do so compared with their older (≥65 y) counterparts, P=0.025. Participants were more likely to download the App if it was free of charge, compared with a version that costs $3, P=0.018. Although only about one third (37.8%) of participants used eye drop reminders, nearly 3 of 4 (72.9%) participants were receptive to using the automated reminder feature of the Glaucoma App. CONCLUSIONS: Glaucoma patients and their caregivers were very interested in using a Glaucoma App; however, many were not willing to spend $3 for an App they seem to value. The free Wills Eye Glaucoma App currently available on the Apple store, includes educational videos, eye drop and appointment reminders, medical and ocular data storage, visual field tutorial, and intraocular pressure tracker. These features aim to increase patients' level of knowledge about glaucoma and improve their adherence to medication and follow-up appointment recommendations.

Simulation Detection in Handwritten Documents by Forensic Document Examiners.

This study documents the results of a controlled experiment designed to quantify the abilities of forensic document examiners (FDEs) and laypersons to detect simulations in handwritten documents. Nineteen professional FDEs and 26 laypersons (typical of a jury pool) were asked to inspect test packages that contained six (6) known handwritten documents written by the same person and two (2) questioned handwritten documents. Each questioned document was either written by the person who wrote the known documents, or written by a different person who tried to simulate the writing of the person who wrote the known document. The error rates of the FDEs were smaller than those of the laypersons when detecting simulations in the questioned documents. Among other findings, the FDEs never labeled a questioned document that was written by the same person who wrote the known documents as "simulation." There was a significant statistical difference between the responses of the FDEs and layperson for documents without simulations.