Documenting motion sequences with a personalized annotation system

Kahol, Kanav; Tripathi, K.; Panchanathan, S.

doi:10.1109/mmul.2006.5

Cited by 27 publications

(18 citation statements)

References 4 publications

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“…This implies that the spatial deployment of body parts is directly linked to the temporal structure outlined in the music (involving rhythm and timing). The modeling and automatic recognition of dance gestures often involve Hidden Markov Modeling (HMM) [7][8][9][10]. However, HMM has the property to exhibit some degree of invariance to local warping (compression and stretching) of the time-axis [11].…”

Section: Spatiotemporal Approachmentioning

confidence: 99%

Dance-the-Music: an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

Maes

Amelynck

Leman

2012

EURASIP J. Adv. Signal Process.

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In this article, a computational platform is presented, entitled "Dance-the-Music", that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers' models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method-can determine the quality of a student's performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures.

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Section: Spatiotemporal Approachmentioning

confidence: 99%

Dance-the-Music: an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

Maes

Amelynck

Leman

2012

EURASIP J. Adv. Signal Process.

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“…Gesture recognition has been investigated for various applications in areas, such as activity recognition and behavior inference [3,4,5], immersive gaming [6,7], and many forms of computer interaction. In this last category, systems have been proposed to replace classical computer input modalities.…”

Section: Related Workmentioning

confidence: 99%

Gesture-Controlled User Input to Complete Questionnaires on Wrist-Worn Watches

Amft

Amstutz

Smailagic

et al. 2009

Human-Computer Interaction. Novel Interaction Methods and Techniques

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Abstract. The aim of this work was to investigate arm gestures as an alternative input modality for wrist-worn watches. In particular we implemented a gesture recognition system and questionnaire interface into a watch prototype. We analyzed the wearer's effort and learning performance to use the gesture interface and compared their performance to a classical button-based solution. Moreover we evaluated the system performance to spot wearer gestures and the system responsiveness. Our wearer study showed that the watch achieved a recognition accuracy of more than 90%. Completion times showed a clear decrease from 3 min in the first repetition to 1 min, 49 sec in the last one. Similarly, variance of completion times between wearers decreased during repetitions. Completion time using the button interface was 36 sec. Ratings of physical and concentration effort decreased during the study. Our results confirm that wearer training state is rather reflected in completion time than recognition performance.

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“…The organization of elementary actions for classifying human movement by describing a hierarchical model has already been studied [33]. The concept of motion primitives was also explored in a method which automatically derived vocabularies of movement modules from visual data by taking advantage of the underlying spatialtemporal structure of human movements [29].…”

Section: Motion Languagementioning

confidence: 99%

Developing a motion language: Gait analysis from accelerometer sensor systems

Sant'Anna¹,

Wickström²

2009

Proceedings of the 3d International ICST Conference on Pervasive Computing Technologies for Healthcare

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Abstract-This work concerns the use of human movement classification as a tool for monitoring and supporting older peoples' lives. The ''motion language" methodology is a movement classification technique which aims at generalizing movements and providing easy interpretation of motion signals by decomposing activities into elementary building blocks referred to as ''motion primitives". The use of motion primitives to classify motion from visual data has been studied. This work shows that the motion language methodology can be applied to acceleration signals, contributing to the development of wearable monitoring systems. This paper explains the development of the motion language and its use in a gait analysis study. Preliminary results show that the motion language methodology can be used to quantitatively measure gait parameters. In addition, motion primitives are shown to express static and dynamic characteristics of different gait patterns and were used to calculate a new symmetry index.

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Documenting motion sequences with a personalized annotation system

Cited by 27 publications

References 4 publications

Dance-the-Music: an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

Dance-the-Music: an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

Gesture-Controlled User Input to Complete Questionnaires on Wrist-Worn Watches

Developing a motion language: Gait analysis from accelerometer sensor systems

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