In this paper, we review how the remote monitoring architectures have evolved over time, paralleling the progress in the Information and Communication Technologies, and describe our experiences with the design of telemedicine systems for blood glucose monitoring in three medical applications. In all of these cases, a remote monitoring system, in charge of delivering the relevant information to the right player, becomes an important part of the sensing architecture. These include the clinical staff, the patient’s significant other, his/her family members, and many other actors helping with the patient treatment that may be located far away from him/her.
Nevertheless, collecting measurements only represents part of the process as another critical task involves delivering the collected measures to the treating specialists and caregivers. Effective bio-sensing technology and advanced signal processing are therefore of unquestioned importance for blood glucose monitoring. Glucose concentration in the blood stream is a critical vital parameter and an effective monitoring of this quantity is crucial for diabetes treatment and intensive care management. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Problem modeling methods are approached based on two means of categorization in this survey. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods.
In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. As far as we know, an overall review of this problem domain has yet to be provided. Several surveys on human pose estimation can be found in the literature, but they focus on a certain category for example, model-based approaches or human motion analysis, etc. Human pose estimation from monocular images has wide applications (e.g., image indexing). Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image.
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