In human pose estimation, a comprehensive evaluation of state-of-the-art frameworks is necessary to advance both research and practical applications. This paper presents a thorough review of state-of-the-art 2D and 3D human pose estimation frameworks, analyzing 118 papers and four GitHub repositories, with a focus on frameworks made since 2019. The following frameworks are chosen based on predefined inclusion criteria: AlphaPose, Detectron2, MediaPipe, MeTRAbs, MHFormer, MMPose, MoveNet, OpenPifPaf, OpenPifPaf-vita, OpenPose, PoseFormerV2, rtmlib, StridedTransformer-Pose3D, ultralytics (YOLOv8), ViTPose, and YOLOv7. This paper evaluates these 16 frameworks on an existing, unpublished dataset consisting of exercise videos recorded with a monocular RGB camera and synchronized gold-standard motion capture data. The dataset includes videos of nine individuals performing eight exercises, recorded from two camera views with different planar angles. The analysis evaluates joint angle performance of the frameworks using weighted mean absolute error and weighted intraclass correlation coefficient as quantitative metrics. MeTRAbs emerged as the best overall framework, while AlphaPose, rtmlib, and YOLOv7 were the top 2D performers.

Visual impairment is a widespread global health issue that affects millions of people across all ages and backgrounds. Timely intervention is essential for the effective management of eye diseases. Smartphones offer the possibility of continuously recording facial gestures during interaction with the device, whereby changes such as squinting of the eyes could indicate progressive vision loss. In this context, a mobile health application was developed to conduct a digital logMAR test while simultaneously capturing real-time facial features. A total of 37 participants took part in a controlled mobile eye test study. The facial landmarks recorded during the test were analyzed to identify patterns that can distinguish between sequences of letters that were read correctly, partially, or not at all. Specifically, explorative data analysis and receiver operating characteristic curves were employed to determine facial landmarks with high discriminative power in relation to reading ability. The predominant facial regions that showed the most significant change under reduced performance during the vision test were the nose, mouth, and cheeks. Notably, the characteristic maximum squinting of the cheeks stood out with an area under the curve of 0.82. The analysis showed the potential of tracking specific facial features for continuous and unobtrusive vision assessment. It motivates to integrate facial feature analysis into an everyday application such as a web browser and to conduct a study in a non-standardized environment on a larger scale.

The COVID-19 pandemic has challenged our day-today life, businesses, and educational institutions by changing the way we interact with each other in a very short space of time. In the context of teaching, lecturers had to rapidly develop concepts and teaching materials that enable distributed virtual and safe learning experiences. In this paper, we describe the challenges of reorganizing a two-week programming course that we have taught for more than ten years in a classroom setting using face-to-face communication. The course teaches students the basics of developing a software system using the Swift programming language and is a prerequisite for a single semester capstone course. We show how we reworked the course into a distributed format using online sessions and real-time feedback for about 80 students. We describe the remote supervision approaches we used to support students and the process of rethinking the course infrastructure by enabling remote access as well as by offering a semi-automated merge management and code review system. Based on this experience, we provide instructors with insights on how to set up and conduct a distributed software engineering course when face-to-face teaching is impossible.

The potential for mHealth solutions is steadily increasing due to an enormous growth in the area of mobile networks and the mobile Internet. However, not only the general connection is becoming faster and more stable, but also the mobile devices themselves are becoming even more advanced. Nowadays, these devices are able to acquire physiological data and transfer them to e.g. a physician or technician to be analyzed before an actual appointment. Using these technological advantages, more and more evidence could be used for diagnosis and treatment. Instead, long preparation and delay are part of everyday practice nowadays and information and data acquisition take up much time before diagnosis.This paper describes a general concept for a centralized screening / pre-diagnosis system for mobile sleep laboratories. The system is designed to have as little influence as possible on the usual sleep environment, but still allows a medically usable recording of sleep activities and health parameters. Furthermore, the concept covers the access possibilities of the attending physician as well as the back-flow of a final diagnosis. Finally, we report on the resulting challenges of such systems with respect to privacy.

OBJECTIVE: Morbidity and mortality due to preeclampsia in settings with limited resources often results from delayed diagnosis. The Congo Red Dot (CRD) test, a simple modality to assess the presence of misfolded proteins in urine, shows promise as a diagnostic and prognostic tool for preeclampsia. We propose an innovative mobile health (mHealth) solution that enables the quantification of the CRD test as a batch laboratory test, with minimal cost and equipment. METHODS: A smartphone application that guides the user through seven easy steps, and that can be used successfully by non-specialized personnel, was developed. After image acquisition, a robust analysis runs on a smartphone, quantifying the CRD test response without the need for an internet connection or additional hardware. In the first stage, the basic image processing algorithms and supporting test standardizations were developed using urine samples from 218 patients. In the second stage, the standardized procedure was evaluated on 328 urine specimens from 273 women. In the third stage, the application was tested for robustness using four different operators and 94 altered samples. RESULTS: In the first stage, the image processing chain was set up with high correlation to manual analysis (z-test P < 0.001). In the second stage, a high agreement between manual and automated processing was calculated (Lin's concordance coefficient ρc = 0.968). In the last stage, sources of error were identified and remedies were developed accordingly. Altered samples resulted in an acceptable concordance with the manual gold-standard (Lin's ρc = 0.914). CONCLUSION: Combining smartphone-based image analysis with molecular-specific disease features represents a cost-effective application of mHealth that has the potential to fill gaps in access to health care solutions that are critical to reducing adverse events in resource-poor settings.

We propose to explicitly model white-spaces for Arabic handwriting recognition within different writing variants. Position-dependent character shapes in Arabic handwriting allow for large white-spaces between characters even within words. Here, a separate character model for white-spaces in combination with a lexicon using different writing variants and character model length adaptation is proposed. Current handwriting recognition systems model the white-spaces implicitly within the character models leading to possibly degraded models, or try to explicitly segment the Arabic words into pieces of Arabic words being prone to segmentation errors. Several white-space modeling approaches are analyzed on the well known IFN/ENIT database and outperform the best reported error rates.