NrityaGuru: A Dance Tutoring System for Bharatanatyam Using Kinect

Aich, Achyuta; Mallick, Tanwi; Bhuyan, Himadri; Das, Partha Pratim; Majumdar, A. K.

doi:10.1007/978-981-13-0020-2_42

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…This involves solutions for three sub-problems as: (a) Segmentation of video and Extraction of Key Frames (b) Recognition of Key Postures in Key Frames (c) Recognition of Adavus as sequences of Key Postures. Hence, we survey the recent 1 An Adavu is a basic unit of Bharatanatyam performance which is used to train the dancers. 2 Momentarily stationary well-defined postures occurs within the Adavu.…”

Section: Related Workmentioning

confidence: 99%

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Recognition of Adavus in Bharatanatyam Dance

Bhuyan

Das

2021

Communications in Computer and Information Science

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Digital heritage has been a challenging problem to preserve the cultural heritage like a dance. Digitization of dance requires systematic analyses and understanding the semantics. However, while several work has been reported in non-Indian Classical Dance (ICD); only few has been done in the ICD because of its complexity. Bharatanatyam is an important ICD form, and Adavus constitute the primary collections of postures and movements in Bharatanatyam. There are 15 classes of Adavus having 52 sub-classes between them. Adavus are the critical building blocks for Bharatanatyam and critical for understanding its artefacts and semantics. Hence, this paper attempts to recognize Adavus based on the sequence of Key Postures (KPs) that define and constitute them. Kinect captures dance performances. The videos are then manually segmented into sequences of Key Frames (KFs) corresponding to the expected KPs. For recognizing KP, we follow two approaches; a) skeleton videos and angles of skeleton bones as features, b) The HOG features from the RGB frames. In both approaches, we train SVMs and recognize the KPs using them. The classifier generated by SVM predicts the sequence of KPs involved in a given Adavu. Since KPs are the string-like encoding symbols of an Adavu, we use the predicted sequence and master sequence of KPs in Edit Distance to recognize the matching Adavu. We use a pre-existing annotated data set of Natta (with eight variants) and Mettu (with four variants) Adavus to achieve over 99% accuracy.

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Section: Related Workmentioning

confidence: 99%

“…They used Kinect to capture RGB, depth and skeleton videos which are synchronized at a rate of 30 fps. The details of the studio set-up, sensors, and tools for data recording are available in [1] and [12]. A part of the data set (Natta Adavus) is available at [13].…”

Section: Data Setmentioning

confidence: 99%

Recognition of Adavus in Bharatanatyam Dance

Bhuyan

Das

2021

Communications in Computer and Information Science

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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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“…Dancing requires high levels of physical skills and body motion control. It has already been reported that dancers strongly benefit from various assistive technologies [ 1 , 2 , 3 , 4 , 5 ]. One of the essential dancing characteristics is the tempo of dancing, dictated by the accompanied music.…”

Section: Introductionmentioning

confidence: 99%

Dance Tempo Estimation Using a Single Leg-Attached 3D Accelerometer

Stančin

Tomažič

2021

Sensors

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We present a methodology that enables dance tempo estimation through the acquisition of 3D accelerometer signals using a single wearable inertial device positioned on the dancer’s leg. Our tempo estimation method is based on enhanced multiple resonators, implemented with comb feedback filters. To validate the methodology, we focus on the versatile solo jazz dance style. Including a variety of dance moves, with different leg activation patterns and rhythmical variations, solo jazz provides for a highly critical validation environment. We consider 15 different solo jazz dance moves, with different leg activation patterns, assembled in a sequence of 5 repetitions of each, giving 65 moves altogether. A professional and a recreational dancer performed this assembly in a controlled environment, following eight dancing tempos, dictated by a metronome, and ranging from 80 bpm to 220 bpm with 20 bpm increment steps. We show that with appropriate enhancements and using single leg signals, the comb filter bank provides for accurate dance tempo estimates for all moves and rhythmical variations considered. Dance tempo estimates for the overall assembles match strongly the dictated tempo—the difference being at most 1 bpm for all measurement instances is within the limits of the established beat onset stability of the used metronome. Results further show that this accuracy is achievable for shorter dancing excerpts, comprising four dance moves, corresponding to one music phrase, and as such enables real-time feedback. By providing for a dancer’s tempo quality and consistency assessment, the presented methodology has the potential of supporting the learning process, classifying individual level of experience, and assessing overall performance. It is extendable to other dance styles and sport motion in general where cyclical patterns occur.

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NrityaGuru: A Dance Tutoring System for Bharatanatyam Using Kinect

Cited by 10 publications

References 11 publications

Recognition of Adavus in Bharatanatyam Dance

Recognition of Adavus in Bharatanatyam Dance

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

Dance Tempo Estimation Using a Single Leg-Attached 3D Accelerometer

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