Background/purpose While haptic simulators in preclinical dentistry show promise, few studies predict novice dental students' performance in conventional simulations using haptic exercises. This study aimed to explore associations between (i) the number of failures in haptic exercises, (ii) the haptic performance index, and (iii) the quality of prosthetic preparation for cast crowns. Additionally, the students' perceptions regarding the use of the VirTeaSy Dental® haptic simulator was analyzed. Materials and methods Forty novice students were randomly selected from the Dental Faculty of Nantes University in September 2022 (mean age: 19.7 ± 1.8 years). They completed four haptic exercises using the VirTeaSy Dental® simulator and prepared cast crowns on pedagogical phantom-mounted models. Data on haptic variables, prosthetic preparation quality scores, and the number of failed/successful haptic exercises were collected. Correlation analyses were conducted, and the mean preparation quality score was compared between students who failed and those who passed the haptic exercises. A questionnaire assessing the students' perceptions when using VirTeaSy Dental® was completed. Results A correlation was found between the number of haptic exercise failures and the prosthetic preparation quality score, with students who failed showing lower scores (10.66 ± 3.69) compared to those who passed (13.72 ± 4.76) (P < 0.05). No correlation was observed for the haptic performance index. Students reported that the VirTeaSy simulator positively impacted their learning of milling gestures. Conclusion The number of haptic exercise failures can predict performance in conventional simulations and help identify students with manual dexterity issues, guiding personalized preclinical training adjustments.

Nowadays, Virtual Learning Environments (VLE) dedicated to learning gestures are more and more used in sports, surgery, and in every domain where accurate and complex technical skills are required. Indeed, one can learn from the observation and imitation of a recorded task, performed by the teacher, through a 3D virtual avatar. In addition, the student's performance can be automatically compared to that of the teacher by considering kinematic, dynamic, or geometric properties. The motions of the body parts or the manipulated objects can be considered as a whole, or temporally and spatially decomposed into a set of ordered steps, to make the learning process easier. In this context, CheckPoints (CPs) i.e. simple 3D shapes acting as "visible landmarks", with which a body part or an object must go through, can help in the definition of those steps. However, manually setting CPs can be a tedious task especially when they are numerous. In this paper, we propose a machine learning-based system that predicts the number and the 3D position of CPs, given some demonstrations of the task to learn in the VLE. The underlying pipeline used two models: (a) the "window model" predicts the temporal parts of the demonstrated motion that may hold a CP and (b) the "position model" predicts the 3D position of the CP for each predicted part from (a). The pipeline is applied to three learning activities: (i) glass manipulation (ii), geometric shapes drawing and (iii), a dilution process in biology. For each activity, the F1-score is equal to or higher than 70% for the "window model", while the Normalized Root Mean Squared Error (NRMSE) is below 0.07 for the "position model".

BACKGROUND:Haptic technologies have opened a new avenue in preclinical dental education, with evidence that they can be used to improve student performance. The aim of this systematic review was to (1) determine the effect of haptic simulators on motor skill acquisition during preclinical dental training, (2) explore students' perception, and (3) explore the ability of haptic systems to distinguish users based on their initial level of manual dexterity. METHODS:A comprehensive search of articles published up to February 2023 was performed using five databases (i.e., PubMed/Medline, ScienceDirect, Web of Sciences, Scopus, and Cochrane Library) and specialized journals. The Preferred Reporting Items for Systematic Review and Meta-Analysis 2020 guidelines were followed, and the risk of bias was assessed. Only studies on the application of haptic simulators in dentistry preclinical training were included. Qualitative synthesis of data was performed, and the protocol was registered in PROSPERO (ID = CRD42022337177). RESULTS:Twenty-three clinical studies, including 1303 participants, were included. The authors observed a statistically significant improvement in dental students' motor skills in various dental specialties, such as restorative dentistry, pediatric, prosthodontics, periodontics, implantology, and dental surgery, after haptic training. Haptic technologies were perceived well by all participants, with encouraging data regarding their ability to differentiate users according to their initial level of manual dexterity. CONCLUSIONS:Our work suggests that haptic simulators can significantly improve motor skill acquisition in preclinical dental training. This new digital technology, which was well perceived by the participants, also showed encouraging results in discriminating users according to their level of experience.

This special issue of the International Journal of Serious Games offers very valuable extensions to the best papers of the 2020 edition of the GaLA conference. The local organization committee was composed of computer scientists of Laval (France), affiliated to Le Mans Université. From the 9th to the 10th of December 2020, 500 participants attended to a well-organized conference, through their virtual avatars, and listened to 37 presentations on Serious Games and Gamification. A special session was related to Virtual Reality, in a pedagogical and gaming context. The four extended papers, published in this journal, significantly extend their original work, and were accepted through a regular peer-review process for this special issue.

This paper discusses the usability of a generic method for the evaluation of the user activity in Virtual Learning Environments (VLE) and its implementation with Unity. In the context of motion-based tasks, the learning process relies on the observation and imitation of the task demonstrated by the teacher. The learner task is compared to the teacher one in terms of: (a) motions shape of the user and the manipulated artefacts and (b), the sequential order of 3D checkpoints that the user must collide with. The integration of the evaluation system into any existing VLE rises challenges regarding the system architecture and the Human Computer Interface to set up the evaluation process. A usability test related to the design of this process is conducted for a pool shooting, a dart throwing and a letter writing simulation. The preliminary results show that: (i) the integration of an existing VLE into the evaluation system is feasible despite issues related to the interaction assets and (ii), all participants are satisfied by their designed evaluation process for pool shooting and dart throwing, they were unable to set up a satisfying evaluation for letter writing due to scale issues.

The analysis of user motions can be useful in many fields to observe human behavior; to follow and predict its action, intention, and emotion; to interact with computer systems; and to enhance user experience in virtual (VR) and augmented reality (AR). These analyses can be empirically made by the expert or with the help of a technology-enhanced learning (TEL) system, allowing the extraction of relevant information from the motion in a pedagogical context. Such analyses are rarely made from 3D captured motions. This can be explained by several factors: the complexity and high dimensionality of the data and the difficulty to correlate the observation and analysis needs of the expert to the extracted data. Machine learning techniques could be used to address some of these problems. In particular, the use of unsupervised learning techniques could help in giving advice according to the analysis of clusters, representing user profiles. During a learning situation, the expert will be assisted in their evaluation task. This work presents two main contributions: (i) the use of clustering techniques to separate motions, into different categories according to a set of well-chosen features, and (ii) the development of a TEL environment using clustering techniques in order to assist the expert in its motion-based evaluation task.

Identification and mapping of trees through a Geo Collaborative Inventory (GCI) platform is an important task for research in botany, citizen sciences and education. The quantity and veracity of the recorded data rely mainly on the motivation and engagement of each participant. However, for a non-botanist, tree mapping can be perceived as an unstructured and tedious task that requires advanced skills. In addition, existing GCI applications deliver poor or nonexistent feedbacks regarding identification and mapping skills as well as progression in these skills. Structuring GCI sessions and enhancing them with game mechanics and clear objectives may have a positive effect on the inventory quality. Inventory situations being highly context dependant, a descriptive model of the GCI task is required to create adapted activity scenarios usable in various situations. This paper presents Albiziapp: a web collaborative and mobile tool, based on OpenSteetMap, that operationalizes any gamified scenario, consistent with a descriptive model built from a structural analysis of the GCI task.

This paper studies the problem of evaluation of human 3d+t activities in Virtual Environments (VE) for Learning (VEL). Current evaluation methods focus mostly on: (i) the automatic identification of an ordered sequence of actions and/or (ii), an empirical analysis made by experts through the VE. In many cases, the learner's activity can be represented by some specific time series made of geometrical data of 3D artefacts. For the extraction and analysis of such Motions Of Interest (MOI), one can manually segment them among the traces, and/or use automatic approaches requiring a database of annotated examples. Both cases usually require too many resources to design such environments. Consequently, this work presents a method allowing teachers to quickly build, compare and evaluate a 3d+t learning activity in VE. This method is based on a semi-automatic approach combining the Dynamic Time Warping algorithm, with 3D reference shapes and few expert's demonstrations of the task to learn.

We learn and/or relearn motor skills at all ages. Feedback plays a crucial role in this learning process, and Virtual Reality (VR) constitutes a unique tool to provide feedback and improve motor learning. In particular, VR grants the possibility to edit 3D movements and display augmented feedback in real time. Here we combined VR and motion capture to provide learners with a 3D feedback superimposing in real time the reference movements of an expert (expert feedback) to the movements of the learner (self-feedback). We assessed the effectiveness of this feedback for the learning of a throwing movement in American football. This feedback was used during (concurrent feedback) and/or after movement execution (delayed feedback), and it was compared with a feedback displaying only the reference movements of the expert. In contrast with more traditional studies relying on video feedback, we used the Dynamic Time Warping algorithm coupled to motion capture to measure the spatial characteristics of the movements. We also assessed the regularity with which the learner reproduced the reference movement along its path. For that, we used a new metric computing the dispersion of distance around the mean distance over time. Our results show that when the movements of the expert were superimposed on the movements of the learner during learning (i.e., self + expert), the reproduction of the reference movement improved significantly. On the hand, providing feedback about the movements of the expert only did not give rise to any significant improvement regarding movement reproduction.

More and more software applications use human motions to improve the information retention. Some virtual environments are especially built to support the learning of human motions. However, these kinds of applications and their pedagogical feedback are rarely made from the analysis of 3D captured motions. This can be explained by the heterogeneity, the complexity and the high-dimensional nature of such data. However, machine learning techniques could be used to overcome these issues. This paper presents a first step towards the improvement of the human learning process of a motion, thanks to the analysis of clusters representing user profiles. In the context of the Bottle Flip Challenge and using raw captured motions, descriptors based on speed and acceleration are extracted. The motions are then automatically analyzed, according to two different approaches: one with the ground truth, and one without constraints on the number of clusters. The results suggest that the data are separable using the computed descriptors.

More and more domains such as industry, sport, medicine, Human Computer Interaction (HCI) and education analyze user motions to observe human behavior, follow and predict its action, intention and emotion, to interact with computer systems and enhance user experience in Virtual (VR) and Augmented Reality (AR). In the context of human learning of movements, existing software applications and methods rarely use 3D captured motions for pedagogical feedback. This comes from several issues related to the highly complex and dimensional nature of these data, and by the need to correlate this information with the observation needs of the teacher. Such issues could be solved by the use of machine learning techniques, which could provide efficient and complementary feedback in addition to the expert advice, from motion data. The context of the presented work is the improvement of the human learning process of a motion, based on clustering techniques. The main goal is to give advice according to the analysis of clusters representing user profiles during a learning situation. To achieve this purpose, a first step is to work on the separation of the motions into different categories according to a set of well-chosen features. In this way, allowing a better and more accurate analysis of the motion characteristics is expected. An experimentation was conducted with the Bottle Flip Challenge. Human motions were first captured and filtered, in order to compensate for hardware related errors. Descriptors related to speed and acceleration are then computed, and used in two different automatic approaches. The first one tries to separate the motions, using the computed descriptors, and the second one, compares the obtained separation with the ground truth. The results show that, while the obtained partitioning is not relevant to the degree of success of the task, the data are separable using the descriptors.

The L-system is a rewriting process based on formal grammar and is used to generate 3D, dynamic structures such as virtual plants and fractal graphics. In previous works, we highlighted that existing L-system software applications and programs are limited, either in terms of human interaction or in terms of modelling. In particular, few of them allow the user to interact with virtual plants during their growth. Our own L-system engine was developed and called the real-time interactive L-system (RTIL-system). The RTIL-system covers most important L-system extensions such as parametric and context-sensitive features. Furthermore, real-time interactions with the user and the environment with respect to L-system formalism are available. This paper presents an RTIL-system focusing on human interaction, the Partial Interactive Derivation (PID) concept and further progress by the extension of PID to context-sensitive rules. To illustrate the potential of the RTIL-system, the effect of various interactive tasks such as sub-axis additions, pruning and bending on the subsequent dynamic development of virtual plants is described.

Humans possess the ability to perform complex manipulations without the need to consciously perceive detailed motion plans. When a large number of trials and tests are required for techniques such as learning by imitation and programming by demonstration, the virtual reality approach provides an effective method. Indeed, virtual environments can be built economically and quickly, and can be automatically reinitialized. In the fields of robotics and virtual reality, this has now become commonplace. Rather than imitating human actions, our focus is to develop an intuitive and interactive method based on user demonstrations to create humanlike, autonomous behavior for a virtual character or robot. Initially, a virtual character is built via real-time virtual simulation in which the user demonstrates the task by controlling the virtual agent. The necessary data (position, speed, etc.) to accomplish the task are acquired in a Cartesian space during the demonstration session. These data are then generalized off-line by using a neural network with a back-propagation algorithm. The objective is to model a function that represents the studied task, and by so doing, to adapt the agent to deal with new cases. In this study, the virtual agent is a 6-DOF arm manipulator, Kuka Kr6, and the task is to grasp a ball thrown into its workspace. Our approach is to find a minimum number of necessary demonstrations while maintaining adequate task efficiency. Moreover, the relationship between the number of dimensions of the estimated function and the number of human trials is studied, depending on the evolution of the learning system.