Conservation of cultural heritage is nowadays a very important aspect of our lives. Thanks to such legacy we gain knowledge about our ancestors, methods of production and ways of their life. The rapid development of 3D technology allows for more and more faithful reflection of this area of life. The rich cultural heritage, both tangible and intangible, can be preserved for future generations due to the use of advanced 3d technologies. They provide the means of documenting, recovering and presenting items of cultural heritage. Not only buildings or monuments are taken into account. An important aspect of our culture is intangible cultural heritage (ICH), including acting, crafting or storytelling, passed down from generation to generation. Due to the rapid development of civilisation and the migration of people, this type of culture is often forgotten. That is why the preservation of ICH is an important element of today world. The main aim of this study, on the basis of the gathered papers, is to identify: (1) the general state of use of 3D digital technologies in ICH; (2) the topics and themes discussed; (3) the technologies used in the study; (4) locations of research centres conducting such studies; and (5) the types of research carried out. The methodology consists of the following main steps: defining study questions, searching query development, selection of publications in Scopus, Web of Knowledge and IEEE Xplore, finally the study execution and the analysis of the obtained results. The results show that for ICH the most often used technologies are: 3D visualisation, 3D modelling, Augmented Reality, Virtual Reality and motion capture systems.

Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.

Traditional dance is one of the key elements of Intangible Culture Heritage (ICH). Many scientific papers concern analysis of dance sequences, classification and recognition of movements, making ICH data public, creating and visualising 3D models or software solutions for learning folklore dances. These works make it possible to preserve this disappearing art. The aim of this article is to propose a methodology for scanning folklore dances. The methodology was developed on the basis of capturing 3D data via an optical motion capture system with a full body Plug-in Gait model that allows for kinematic and kinetic analysis of motion sequences. An additional element of this research was the development of a hand model with which it is possible to precisely analyse the fingers, which play a significant role in many dances. The present methodology was verified on the basis of the Lazgi dance, included in the UNESCO ICH list. The obtained results of movement biomechanics for the dance sequence and the angles of the fingers indicate that it is universal and can be applied to dances that involve the upper and lower body parts, including hand movements.

The purpose of this study is to compare recreationally physically active females and males with regard to spine, pelvis and lower limb joints peak angles in each plane of motion during a single leg squat (SLS). The second aim is to investigate the relationship between kinematics and SLS depth in females and males. Fifty-eight healthy, young adults performed 5 repetitions of a single right leg squat to maximal depth while keeping their balance. Kinematic data were obtained using an optical motion capture system. At the hip, greater adduction and greater internal rotation were observed in females than in males. Females had more extended spines and less outward bended knees throughout the SLS than did men. In males, squat depth was significantly, positively correlated with the maximal angle of the ankle (r = 0.60, p < 0.001), the knee (r = 0.87, p < 0.001), the hip (r = 0.73, p < 0.001) and the pelvis (r = 0.40, p = 0.02) in the sagittal plane. A positive significant correlation was found between SLS depth and maximal angle of the knee (r = 0.88, p < 0.001) and the ankle (r = 0.53, p = 0.01) in the sagittal plane in females. Males and females used different motor strategies at all levels of the kinematic chain during SLS.

Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters.