Dance Training Tool Using Kinect-Based Skeleton Tracking and Evaluating Dancer’s Performance

Muangmoon, Ob-orm; Sureephong, Pradorn; Tabia, Karim

doi:10.1007/978-3-319-60045-1_4

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…It does not require training data and experience of experts. In this study, we assumed the upper bound was 90 degrees instead of 180 degrees based on an earlier study [18] and our practical exercise scenario.…”

Section: Discussionmentioning

confidence: 99%

“…Performance score = ∑k = 1s[1−C(sik, tjk)90×8]s = 1−DTW(S, T)90×8×s, where s is the length of optimal warping path, 8 stands for eight bone vectors selected for motion evaluation, and C ( s ik , t jk ) is the element of optimal warping path in the cost matrix, which is the summation of angle differences for eight bone vectors, and DTW distance- DTW ( S , T ) is a summation of elements ( C ( s ik , t jk )) along the optimal path. We assume the angle difference between two corresponding bone vectors is within 90 degrees based on an earlier study [18], which results in the maximum DTW ( S , T ) along the optimal path, which would be 90 × 8 × s. Because the output distance ( DTW ( S , T )) is a measure of dissimilarity between the two motion time series (the longer the distance, the greater the deviation), the last part of Equation (4) would be a percentage score (0–100%) to measure the level of similarity between the trainee’s motion and trainer’s motion.…”

Section: Methodsmentioning

confidence: 99%

“…They decomposed each form of Tai Chi into four poses and calculated the difference of joint angles between skeletons of standard pose and user’s actual pose, which were tracked by Kinect sensors. Muangmoon et al [18] adopted a similar method to evaluate Thai dance performance. Even though this kind of motion comparison algorithm is easy to implement due to the simplification of extracting a few discrete poses from continuous motions, the quality of exercise could not be evaluated comprehensively.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Time Warping Based Algorithm to Evaluate Kinect-Enabled Home-Based Physical Rehabilitation Exercises for Older People

Xiong

2019

Sensors

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Older people face difficulty engaging in conventional rehabilitation exercises for improving physical functions over a long time period due to the passive nature of the conventional exercise, inconvenience, and cost. This study aims to develop and validate a dynamic time warping (DTW) based algorithm for assessing Kinect-enabled home-based physical rehabilitation exercises, in order to support auto-coaching in a virtual gaming environment. A DTW-based algorithm was first applied to compute motion similarity between two time series from an individual user and a virtual coach. We chose eight bone vectors of the human skeleton and body orientation as the input features and proposed a simple but innovative method to further convert the DTW distance to a meaningful performance score in terms of the percentage (0–100%), without training data and experience of experts. The effectiveness of the proposed algorithm was validated through a follow-up experiment with 21 subjects when playing a Tai Chi exergame. Results showed that the algorithm scores had a strong positive linear relationship (r = 0.86) with experts’ ratings and the calibrated algorithm scores were comparable to the gold standard. These findings suggested that the DTW-based algorithm could be effectively used for automatic performance evaluation of an individual when performing home-based rehabilitation exercises.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dynamic Time Warping Based Algorithm to Evaluate Kinect-Enabled Home-Based Physical Rehabilitation Exercises for Older People

Xiong

2019

Sensors

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“…Currently, there are multiple dancing ITSs [6,7,8,9,10,17,18]; however, to the best of our knowledge each of the systems is designed to support just one specific dance style such as the system described in Reference [17] that exclusively supports Korean pop dancing or the system in Reference [18] that exclusively supports Forró. With this common approach, to build a new ITSs for dancing one has to start from scratch.…”

Section: Dancing Coach Applicationmentioning

confidence: 99%

“…There are also some MMLA ITSs designed to train people to dance traditional dances such as ballet [6], Bharatanatyam (traditional Indian dance) [7], salsa [8,9], Thai dance [10], and so forth. Many of these ITSs [6,7,8,10] use depth cameras first to capture an expert performing a dance movement and use the expert’s postures as a baseline. Learners then are expected to mimic the recorded performance of the experts while being tracked by a depth camera.…”

Section: Introductionmentioning

confidence: 99%

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

Romano

Schneider

Drachsler

2019

Sensors

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Dancing is an activity that positively enhances the mood of people that consists of feeling the music and expressing it in rhythmic movements with the body. Learning how to dance can be challenging because it requires proper coordination and understanding of rhythm and beat. In this paper, we 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. However, the DC has been designed to allow the addition of more dance styles. We also present the first user evaluation of the DC, which consists of user tests with 25 participants. Results from the user test show that participants stated they had learned the basic salsa dancing steps, to move to the beat and body coordination in a fun way. Results also point out some direction on how to improve the future versions of the DC.

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