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Gait recognition has been of great importance for disease diagnosis, rehabilitation assessment, as well as personal identification. Conventional gait analysis generally has to rely heavily on complex, expensive data acquisition and computing apparatus. To significantly simplify the evaluation process the mobile phone, which is one of the most indispensable electronic media in human daily life, was adopted as a pervasive tool for gait study, by using its digital imaging recording and analysis function. The basic procedure to record and quantify the video of human gait was illustrated and demonstrated through conceptual experiments. Potential applications were discussed. Some fundamental and practical issues raised in such flexible technology were pointed out. This method is expected to be widely used in future human analysis.

Telecystoscopy can lower the barrier to access critical urologic diagnostics for patients around the world. A major challenge for robotic control of flexible cystoscopes and intuitive teleoperation is the pose estimation of the scope tip. We propose a novel real-time camera localization method using video recordings from a prior cystoscopy and 3D bladder reconstruction to estimate cystoscope pose within the bladder during follow-up telecystoscopy. We map prior video frames into a low-dimensional space as a dictionary so that a new image can be likewise mapped to efficiently retrieve its nearest neighbor among the dictionary images. The cystoscope pose is then estimated by the correspondence among the new image, its nearest dictionary image, and the prior model from 3D reconstruction. We demonstrate performance of our methods using bladder phantoms with varying fidelity and a servo-controlled cystoscope to simulate the use case of bladder surveillance through telecystoscopy. The servo-controlled cystoscope with 3 degrees of freedom (angulation, roll, and insertion axes) was developed for collecting cystoscope videos from bladder phantoms. Cystoscope videos were acquired in a 2.5D bladder phantom (bladder-shape cross-section plus height) with a panorama of a urothelium attached to the inner surface. Scans of the 2.5D phantom were performed in separate arc trajectories each of which is generated by actuation on the angulation with a fixed roll and insertion length. We further included variance in moving speed, imaging distance and existence of bladder tumors. Cystoscope videos were also acquired in a water-filled 3D silicone bladder phantom with hand-painted vasculature. Scans of the 3D phantom were performed in separate circle trajectories each of which is generated by actuation on the roll axis under a fixed angulation and insertion length. These videos were used to create 3D reconstructions, dictionary sets, and test data sets for evaluating the computational efficiency and accuracy of our proposed method in comparison with a method based on global Scale-Invariant Feature Transform (SIFT) features, named SIFT-only. Our method can retrieve the nearest dictionary image for 94–100% of test frames in under 55ms per image, whereas the SIFT-only method can only find the image match for 56–100% of test frames in 6000–40000ms per image depending on size of the dictionary set and richness of SIFT features in the images. Our method, with a speed of around 20 Hz for the retrieval stage, is a promising tool for real-time image-based scope localization in robotic cystoscopy when prior cystoscopy images are available.

All health-related issues exist in a context of extending health expectancy. Behavioral risk factors, diagnostic ”omics,” disparities, insurance, tissue engineering, and climate can shorten life expectancy, but before that, health expectancy. Longer life can bring decades of disability; longer health can mean dying healthy after brief incapacity. Because health precedes other accomplishments, extending average health expectancy into the ninth decade during the $21^{\mathrm{\text{st}}}$ century would have an impact comparable to doubling life expectancy in the $20^{\mathrm{\text{th}}}$ century.

Objective: To conduct a preliminary study of the hierarchical diagnosis and treatment of patients with obstructive sleep apnea/hypopnea syndrome (OSAHS) using the Internet of Things (IoT) medical technology and to explore the feasibility of the hierarchical diagnosis, treatment, and management of OSAHS patients using IoT medicine in primary hospitals.

Methods: The IoT technology and a remote medical monitoring system were used to observe and compare the respiratory and sleep parameters before and after a three-month intervention in 47 patients with OSAHS who met the diagnostic criteria and were selected in the Kashgar region. All parameters were compared based on the severity (mild, moderate, and severe) of OSAHS.

Results: The Epworth Sleepiness Scale (ESS) score, apnea–hypopnea index (AHI), and nighttime minimum oxygen saturation (lowest SaO₂min) improved in patients with OSAHS from before to after treatment ($p<0.05$). The improvements were more profound in OSAHS patients with cardiovascular disease such as hypertension.

Conclusion: The IoT medical technology can help to hierarchically diagnose, treat, and manage patients with OSAHS. It is feasible for primary hospitals in rural regions to use the IoT technology for the hierarchical diagnosis and treatment of OSAHS patients.

Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.