### Article
Limb analysis [28] has also become a common feature [29], leading to custom applications for feedback training employing specific variables, such as a single joint moment or angle. To make such computation possible, approximations that ignore certain mechanical factors, such as inertial components in equations of motion, are frequently utilized, often applying some deep-learning model for compensation. Applications range from computer-vision-based cerebral infarction rehabilitation [30], head neck rehabilitation [31], spinal-cord injury [32], stroke rehabilitation [33], avoidance or alleviation of fracture pains [34], port medicine [35] to robotic-induced systems, where skeleton tracking-based posture assessment can be used for real-time monitoring of upper limb rehabilitation [36]. Naturally, such techniques also apply to a number of other related real-world applications [37], ranging from augmented reality [38] to bad-posture detection [39; 40], sitting-posture sensing [41], occluded-pose reconstruction [42] and other modern health-related applications. It is evident that computer-vision research has produced a wide range of marker-based and markerless technologies in recent years, with the potential to be utilized in a wide range of disciplines and settings. The following issues, however, must still be addressed:  
* Requirements for a markerless motion-data acquisition systems remain reliant on the research field and the unique physical-acquisition settings, and so differ between disciplines [43]. * Human motion analysis systems should be very precise to detect minute changes in motion in sports biomechanics and physical rehabilitation tasks, as well as adaptable, noninvasive, and free of constraints [44]. It should be emphasized that resolution (both spatially and temporally) has the same effect on markerless systems as it does on marker-based systems [45]. * One must also acknowledge that the bulk of the data recorded will be significantly bigger, and so, the markerless systems may need to forego accuracy in order to construct a deployable, rapid system, which may pose difficulty when performing a proper medical analysis. Machine learning for this type of system will almost certainly entail the procurement of a costly graphical processing unit (GPU)-oriented frame processing machine in order to handle vast volumes of video data accurately and efficiently, thus stopping the actual deployment [46]. The main novelty of this paper is a novel deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present a proprietary plonynomial Pareto-optimized deep-learning architecture for processing inverse kinematics for sorting out and rearranging human skeleton joints generated by RGB (red, green, blue) image-based two-dimensional (2D) skeleton recognition algorithms, such as blazepose [47], with the goal of producing a full 3D model as a final result. _Our model differs from other implementations in its activity-independent architecture while still ensuring anthropometric regularity and retaining high joint mapping accuracy with smooth motion frames._ The proposed approach allows to extract the entire humanoid character motion curve, which can then be bound to a 3D mesh for preview in near real time. In addition, because the entire video feed is treated as a single entity instead of processing on a frame-by-frame basis, this allows for smooth interpolation between poses, where the interpolation accuracy can be managed by the video-feed sampling rate. The sampling rate can be lowered for faster video preprocessing in exchange for accuracy and vice versa, allowing all calculations to be performed on CPU-based processing systems, rather than expensive GPU farm equipment. The paper is organized as follows.