The task of teleoperating a robot over a wireless video link is known to be very difficult. Teleoperation becomes even more difficult when the robot is surrounded by dense obstacles, or speed requirements are high, or video quality is poor, or wireless links are subject to latency. Due to high quality lidar data, and improvements in computing and video compression, virtualized reality has the capacity to dramatically improve teleoperation performance -even in high speed situations that were formerly impossible. In this paper, we demonstrate the conversion of dense geometry and appearance data, generated on-the-move by a mobile robot, into a photorealistic rendering database that gives the user a synthetic exterior line-of-sight view of the robot including the context of its surrounding terrain. This technique converts remote teleoperation into line-of-sight remote control. The underlying metrically consistent environment model also introduces the capacity to remove latency and enhance video compression. Display quality is sufficiently high that the user experience is similar to driving a video game where the surfaces used are textured with live video.

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BACKGROUND Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person’s home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations. OBJECTIVE This study aimed to propose a machine learning–based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns. METHODS Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis. RESULTS In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (<i>P</i>=.04), glycated hemoglobin level (<i>P</i>&lt;.001), and time in range (<i>P</i>&lt;.001) and thus had different management needs. CONCLUSIONS The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients’ GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.