# Investigating the Learning Process of Folk Dances Using Mobile Augmented Reality
## 1 Introduction  
Augmented reality (AR) has the ability to integrate virtual information into the real environment in real time performance [1]. Nowadays, with simultaneous localization and mapping (SLAM) algorithms, the integration is easier and gives the illusion that real and virtual objects co-exist in the same space. One of the important applications of AR is in the entertainment domain. Dance is a performing art and it consists of sequences of different movements. It can have various messages according to context, e.g., traditional dances are strongly connected to culture and culture heritage of place or nation [2]. Dances are mainly taught in two ways; attending dance lesson or self-learning by watching and imitating demonstrations presented in video or the three-dimensional (3D) virtual environment [3]. Major drawback of video or online websites, where dances can be found is a lack of interactive feedback but virtual reality (VR) and/or AR technology can overcome these limitations [4; 5]. These approaches can also improve interaction and enable more functionalities, e.g., zoom in, watch the dancer from different angles, get feedback.  
One the other hand, an obvious advantage of the AR application is that numerous dances can be added to the same application so users can learn many dances this way. Typically, teacher or professional dancer is recorded using different motion capture systems [6] and animations are added to avatars available in the application. During learning procedure, users' movements are also captured, compared to the teacher's and feedback is provided or captured data are stored and used for further offline analysis. Except providing additional functionalities and feedback, AR applications for dance learning can be used whenever and wherever users want to learn and practice on their own pace.  
In this paper, a prototype mobile AR application for learning folk dances is presented and evaluated in laboratory conditions. An avatar of the professional dancer is shown to the users and they were instructed to imitate movements and try to learn the dance. Their movements are captured using optical motion capture system with passive markers. Functionalities like play/pause, switch between showcase and learning mode, change the speed, switch between different movements are implemented. It is possible to move avatar in space and examine the dance from different points. The AR application contains male and female version of one folk dance, Pasyovska Sedlcka, was assessed by comparing it with a projection mode to examine whether it can assist process of learning. The pilot study that was conducted before the experiment showed that the time span of 10 min should be enough for training since the chosen dance is around 25 s long. After training, participants performed the dance three times just with music and the data were recorded using the same motion capture system. Our hypothesis is that mobile AR application is better for learning how to dance compared to a large projector screen. Data filtered using cubic interpolation is exported and analyzed using dynamic time warping (DTW) [7]. For each body joint, of each participant and professional dancer, change between rotations for each frame was calculated and stored. After that changes for corresponding joints were compared to the data from professional dancers using DTW and average score was calculated in order to get the score for the whole body. Experimental results of 20 participants indicate that that AR application shows greater potential to be used for learning process. The workflows for learning procedure and data analysis are presented on Figures 1 and 2.