Dancing Salsa with Machines—Filling the Gap of Dancing Learning Solutions

Romano, Gianluca; Schneider, Ján; Drachsler, Hendrik

doi:10.3390/s19173661

Cited by 17 publications

(16 citation statements)

References 19 publications

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“…Sensors are increasingly becoming portable and smarter, enabling efficient methods for the acquisition of performance data, which allows for effective monitoring and intervention. Such sensors have been explored to provide support in various learning domain [8,15,[17][18][19]. Schneider et al [20] analysed 82 prototypes found in literature studies based on Bloom's taxonomy of learning domains and explored how they can be used to provide formative assessment, especially as a feedback tool.…”

Section: Sensors In Sportsmentioning

confidence: 99%

“…Data from smartphones have been used to predict human activities such as movement [ 10 , 11 ] and fall detection [ 12 ]. Another external sensor used for training psychomotor skills is Microsoft Kinect [ 13 , 14 , 15 ]. The Kinect is an external infrared sensor that is used to track skeletal points of the human body and their movements, which can also be useful in the case of table tennis.…”

Section: Introductionmentioning

confidence: 99%

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Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks

Sanusi

Mitri

Limbu

et al. 2021

Sensors

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Beginner table-tennis players require constant real-time feedback while learning the fundamental techniques. However, due to various constraints such as the mentor’s inability to be around all the time, expensive sensors and equipment for sports training, beginners are unable to get the immediate real-time feedback they need during training. Sensors have been widely used to train beginners and novices for various skills development, including psychomotor skills. Sensors enable the collection of multimodal data which can be utilised with machine learning to classify training mistakes, give feedback, and further improve the learning outcomes. In this paper, we introduce the Table Tennis Tutor (T3), a multi-sensor system consisting of a smartphone device with its built-in sensors for collecting motion data and a Microsoft Kinect for tracking body position. We focused on the forehand stroke mistake detection. We collected a dataset recording an experienced table tennis player performing 260 short forehand strokes (correct) and mimicking 250 long forehand strokes (mistake). We analysed and annotated the multimodal data for training a recurrent neural network that classifies correct and incorrect strokes. To investigate the accuracy level of the aforementioned sensors, three combinations were validated in this study: smartphone sensors only, the Kinect only, and both devices combined. The results of the study show that smartphone sensors alone perform sub-par than the Kinect, but similar with better precision together with the Kinect. To further strengthen T3’s potential for training, an expert interview session was held virtually with a table tennis coach to investigate the coach’s perception of having a real-time feedback system to assist beginners during training sessions. The outcome of the interview shows positive expectations and provided more inputs that can be beneficial for the future implementations of the T3.

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Section: Sensors In Sportsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks

Sanusi

Mitri

Limbu

et al. 2021

Sensors

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“…All decisions obtained in this comparison are presented in Table 7 on the basis of both Expert System (with system warnings) and trainer. In order to facilitate the procedures, good and moderate movements of the participants in the trainer evaluations were accepted as the correct movements as in [88,89]. In Table 7, the decisions taken by the system are matched by the decisions of the trainer by 89% in DSP training, 82% in LR training, and 85.5% in total.…”

Section: Decisions Of Rule-based Expert Systemmentioning

confidence: 99%

Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

Örücü

Selek

2020

Applied Sciences

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In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a 28-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (25.8 ± 5.47 years; 74.69 ± 14.81 kg; 173.5 ± 9.52 cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired t-test analysis (p < 0.05) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by 58.08 ± 11.32% in LR training and 54.84 ± 12.72% in DSP training.

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“…Dancing, which consists of feeling the music and expressing it in rhythmic movements of the body, is an activity that positively enhances the mood of people. In paper [13], Romano et al present the first implementation of the Dancing Coach (DC), a generic system designed to support the practice of dancing steps which, in its current state, supports the practice of basic salsa dancing steps. Results from the user test show that participants stated they had learned the basic salsa dancing steps, allowing them to move to the beat and use body coordination in a fun way.…”

Section: Contributionsmentioning

confidence: 99%

Advanced Sensors Technology in Education

Crespo

Burgos

2019

Sensors

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The topic presented will show how different kinds of sensors can help to improve our skills in learning environments. When we open the mind and let it take the control to be creative, we can think how a martial art would be improved with registered sensors, or how a person may dance with machines to improve their technique, or how you may improve your soccer kick for a penalties round. The use of sensors seems easy to imagine in these examples, but their use is not limited to these types of learning environments. Using depth cameras to detect patterns in oral presentations, or improving the assessment of agility through low cost-sensors with multimodal learning analytics, or using computing devices as sensors to measure their impact on primary and secondary students’ performances are the focus of this study as well. We hope readers will find original ideas that allow them to improve and advance in their own researches.

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Dancing Salsa with Machines—Filling the Gap of Dancing Learning Solutions

Cited by 17 publications

References 19 publications

Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks

Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks

Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

Advanced Sensors Technology in Education

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