In this paper, an overlapping-resistant Internet of Things (IoT) solution for a Bluetooth Low Energy (BLE)-based indoor tracking system (BLE-ITS) is presented. The BLE-ITS is a promising, inexpensive alternative to the well-known GPS. It can be used in human traffic analysis, such as indoor tourist facilities. Tourists or other customers are tagged by a unique MAC address assigned to a simple and energy-saving BLE beacon emitter. Their location is determined by a distributed and scalable network of popular Raspberry Pi microcomputers equipped with BLE and WiFi/Ethernet modules. Only simple triggered messages in the form of login records (LRs) are sent to a server, where the so-called path vectors (PVs) and interest profile (IPr) are set. The authors implemented the prototype and demonstrated its usefulness in a controlled environment. As it is shown in the paper, the solution is highly overlap-resistant and mitigates the so-called multilocation problem.

A novel motion atlas consisting of 56 different motions was constructed to meet needs of virtual reality (VR) video games. Within the atlas four motion categories were defined: head movements ( HEAD ), hand and arm movements ( ARMS ), whole body movements ( BODY ), and animations ( ANIM ). The data identifying the motion patterns were collected exclusively using VR system peripherals, namely goggles and controllers – for motion capture (MoCap) purposes, the HTC Vive Pro and Meta Quest 2 devices were used. By employing popular machine learning (ML) architectures, 300 motion recognition models were trained, and the most effective ones were selected. The study included classical algorithms such as k-nearest neighbors (kNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), extra-trees classifier (Ensemble), random forests (RF), naive Bayes classifier (NB), and LightGBM (LGBM), which were selected based on literature review. Deep learning (DL) algorithms were also tested: convolutional neural network (CNN), transformer, and long-short-term memory (LSTM). Despite the significantly larger size of the motion atlas compared to other approaches and the limitation to naturally available data within VR systems, the best obtained CNN model achieved a weighted F-score of nearly 98% for motion recognition.

The paper aims to present the concepts of an innovative, integrated visitor support system for distributed entertainment parks, based on Internet of Things (IoT) technology and Big Data analysis. The proposed system will include logistical functions to streamline the customer service process in the centers, offering a profiled tourist product based on unique natural or thematic value. Basing on the modular structure, implementation and integration of the modern IT network, tracking and monitoring technologies, Fog Computing, decision support system and Big Data concepts, it is planned to create a flexible, scalable, reliable, fault tolerant and high security product, corresponding to the expectations of the potential recipients.

A standardized observational instrument designed to measure change in gross motor function over time in children with cerebral palsy is the Gross Motor Function Measure (GMFM). The process of evaluating a value for the GMFM index can be time consuming. It typically takes 45 to 60 min for the patient to complete all tasks, sometimes in two or more sessions. The diagnostic procedure requires trained and specialized therapists. The paper presents the estimation of the GMFM measure for patients with cerebral palsy based on the results of the Zebris FDM-T treadmill. For this purpose, the regression analysis was used. Estimations based on the Generalized Linear Regression were assessed using different error metrics. The results obtained showed that the GMFM score can be estimated with acceptable accuracy. Because the Zebris FDM-T is a widely used device in gait rehabilitation, our method has the potential to be widely adopted for objective diagnostics of children with cerebral palsy.

Major modern theme parks, consisting of tens or even close to one hundred of tourist attractions, are growing continually more complex. The operation of such parks is becoming increasingly more difficult which makes developing their management systems a very challenging endeavor. More so when many different smart objects like cameras, mobile terminals and various sensors (e.g. RFIDs) are deployed throughout the environment which must be integrated into the system. Moreover, in order for the system to be conformable with the concept of Internet of Things and with the idea of Smart City, it should make an intelligent use of a distributed sensor network and provide smart capabilities, i.e. improving the process of tourists management within a smart territory, e.g. automatic congestion avoidance. Implementing such algorithms which involve Big Data analytics can be a very demanding task, especially when the system is required to be scalable to support a huge number of smart devices. Therefore the ability to accurately simulate theme park and test many different scenarios (e.g. distinct hardware configurations, varying positions of attractions, contrasting tourists’ actions) becomes imperative. In this paper we describe a method for simulating IoT-based theme parks. Presented methodology integrates several models: tourists behavior model, tourist attractions model, theme park model and simulation model. Our goal is to create a computer simulation which is able to efficiently model smart theme park.

We present a new toolset named MUN (Metaverse Unity Networking) based on AI algorithms for creating multiplayer VR environments, developed on the basis of user-centered design research. It includes a Unity asset enabling creation and configuration of the multiplayer mode in a VR setup, supplemented with 1) a recommendation engine for optimal programming methods, with error detection in application source code, 2) a user behavior analytics system in multiplayer mode, enhancing realism in avatar and NPC actions using a motion atlas and motion recognition. Developed using Python 3.9,. NET, and Unity v. 2021, it was validated by Unity developers through three rounds of usability tests (N=64).