Accuracy Evaluation of 3D Pose Reconstruction Algorithms Through Stereo Camera Information Fusion for Physical Exercises with MediaPipe Pose.

In recent years, significant research has been conducted on video-based human pose estimation (HPE). While monocular two-dimensional (2D) HPE has been shown to achieve high performance, monocular three-dimensional (3D) HPE poses a more challenging problem. However, since human motion happens in a 3D space, 3D HPE offers a more accurate representation of the human, granting increased usability for complex tasks like analysis of physical exercise.

Sensing In Exergames for Efficacy and Motion Quality: Scoping Review of Recent Publications.

Background: Many studies have shown a direct relationship between physical activity and health. It has also been shown that the average fitness level in Western societies is lower than recommended by the World Health Organization. One tool that can be used to increase physical activity for individual people is exergaming, that is, serious games that motivate players to do physical exercises.

Multiple instance learning framework can facilitate explainability in murmur detection.

Objective: Cardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still, they are often overlooked as their detection and correct clinical interpretation require expert skills.

Automatic Generation of Labeled Data for Video-Based Human Pose Analysis via NLP applied to YouTube Subtitles.

With recent advancements in computer vision as well as machine learning (ML), video-based at-home exercise evaluation systems have become a popular topic of current research. However, performance depends heavily on the amount of available training data. Since labeled datasets specific to exercising are rare, we propose a method that makes use of the abundance of fitness videos available online.

Factors of parental investment in the home language environment in peri-urban China: A mixed methods study.

The home language environment is a critical point of investment in early language skills. However, few studies have quantitatively measured the home language environment of low-socioeconomic-status households in non-western settings. This mixed methods study describes the home language environment and early child language skills among households in a low-socioeconomic-status, peri-urban district of Chengdu, China, and identifies factors influencing parental investment in the home language environment.