Vocalizing dance movement for interactive sonification of laban effort factors

ABSTRACTThis article forms part of a larger qualitative, conceptual project that investigates the ways in which Laban Movement Analysis (LMA) can contribute to the larger field of dance research and education with reference to dance, new technologies and particularly notions of space in screendance. Screendance refers to a dance that is specifically made for the camera or the screen, or rendered in either film, video or digital technologies, resulting in a form that is both screen related and kinesthetic. This article aims to introduce an alternative vocabulary for observing and analysing the ways in which screendance and specifically, Rosas danst Rosas, utilises space. Observation within the context of LMA refers to recognising changes regarding the Body, Effort, Shape and Space patterns observable in a mover. Therefore, this observational and analytical approach facilitates an awareness regarding the specific spatial, temporal and dynamic qualities attributed to the movements, gestures and expressions perceived in Rosas danst Rosas. This article demonstrates the applicability and efficacy of LMA as a framework for observing and analysing screendance as a hybrid of dance and screen, with reference to space in Rosas danst Rosas.

Motion is a fundamental perceptual channel, and we derive substantial information about the world around us from how things move in that environment, informing both cognitive and affective interpretation. While previous information visualization research has explored how motion can represent data, support pattern-matching and ease transitions [2, 13, 15], how to represent the rich expressive and affective capacity of animation remains a challenge. A history of expressive movement in the performative and visual arts offers insight into how movement patterns may carry meaning, yet current research provides only a limited understanding of affective motion features and the potential for expressive movements to be abstracted and applied in other contexts. Descriptive frameworks of human movement such as Laban Movement Analysis (LMA) provide rich analytical tools and principles that model the expressive capacity of movement but these remain descriptive rather than computationally tractable. In this paper we present an enhanced computational model for expressive motion that we developed with movement experts. We briefly describe the results of an ongoing study with choreographers and performers in using these features to express movement qualities and discuss potential applications for information visualization.

Laban Movement Analysis: In Dance Education

We present in this paper an automated dance evaluation method based on dynamic time warping and Laban movement analysis. Our method can evaluate pose similarity, timing accuracy and stylistic quality. We validated our methodology using 400 dance fragments with varying proficiency levels, and achieved 97% accuracy.

The scientific basis of human movement has been studied by dance students, teachers, and researchers in the United States since Margaret H'Doubler established the first dance major at the University of Wisconsin in 1926. Since then, students, faculty, practitioners, and researchers have applied a scientific line of inquiry toward dance movement and training. In the 1980s, medical practitioners and researchers involved with dance populations joined this scientific approach movement. This article is a descriptive study that catalogs and details peer-reviewed articles under the topic headings of teaching and technique. With 229 listed publications, teaching and technique are popular topics. This study delimits analysis of these publications to those in peer-reviewed journals, narrowing the field to 59 articles. In this study, research was organized three ways: 1. by the type of research (descriptive studies, analytical studies, experimental studies), 2. by the identification of interests (area of interest and agent of change, item of interest and type of analysis, dependent and independent variables), and 3. into four main themes (participant health, teaching effectiveness, quality of learning and performance, and the field of dance education). Findings are organized into tables for easy navigation and reference. It is hoped that accessibility to and easy navigation through this research will provide teachers with information and support toward informed choices and healthy classrooms. The ultimate aim is to provide a starting place from which dance students, educators, and researchers can develop their own dance medicine and science inquiries into teaching and technique.

In emotion research, a sophisticated framework for how emotions are expressed through the face is beginning to emerge, but our understanding of how the dynamic human body transmits this information is extremely limited. Dancers have been found to show superior accuracy in recognising emotion from whole-body movement displays in comparison with non-dancer controls, but it is currently unclear why this occurs. The present study aims to bridge this gap by exploring the relationship between emotion recognition and visual fixation patterns in three different groups: professional dancers, amateur dancers, and non-dancer controls. In this study, participants watched a series of 5–6▒s dance sequences and completed a binary-choice emotion recognition task while eye-movement data was collected. Professional dancers were significantly more accurate in identifying dance movements with a positive valence when compared with the non-dancer group but showed the poorest overall recognition of negative valence. No significant differences emerged between groups for visual-fixation on any of the target features; rather, all groups appeared to adopt holistic visual-processing strategies. These results are discussed in relation to response bias, the methodological limitations of data collection, and potential observation strategies used by participants. Subjective perceptions of emotion are also explored in relation to quantitative features of movement (through Laban Movement Analysis). Finally, suggestions for future research are presented based on gaps that still exist within the literature.

Effort, commonly understood as the power of an action toward an intended goal, is acknowledged as an important aspect of music expressivity. Previous studies in Hindustani Dhrupad vocal improvisation, particularly those focusing on manual interactions with imaginary objects, have revealed the intricate connection between effort and various movement and melodic variables. The study employed manual annotations by participants who visually inspected and assessed the amount of effort that such interactions were perceived to require. However, since effort is inherently perceptual and subjective and the way that an observer makes assessments on effort levels remains a non-transparent process, the paper seeks to examine the applicability of the Laban Movement Analysis (LMA) system in this task. For this, it relies on a multi-way analysis of variance (ANOVA) to infer manually annotated (numerical) effort levels from Laban’s (categorical) Effort Factors, namely Weight, Flow, Time, and Space, for two Dhrupad performances. The results suggest that apart from the Space factor, which was excluded for reasons delineated, a good part of effort’s variance can be explained through the remaining three statistically significant Effort Factors, leading to the rejection of the null hypothesis that they are unrelated. By ascertaining this relationship, effort-related melodic aspects in Dhrupad improvisation can be predicted using the three Laban Effort Factors.

This paper presents a literature review with an in-depth examination of 17 selected works focusing on the utilization of pose estimation and deep learning models within the domain of dance and movement learning. To address the overarching question regarding the potential implications and utilization of these technologies in teaching movement in dance, the selected literature is systematically analyzed across four key categories: study design/research activity, applied technology and functions, system output in regard to visualization and feedback and study results. Through this structured analysis, recurring themes and patterns within the literature are identified inductively. Subsequently, the findings are synthesized and interpreted, facilitating discussion and connections with existing research. This systematic examination offers insights into the multifaceted role of AI-based technologies in dance education, paving the way for informed pedagogical practices and future research endeavors in this domain.

This paper primarily explores the current status and development pathways of artificial intelligence (AI) technology in dance education. The research methodology is based on literature analysis. The study suggests that AI has great potential in areas such as dance movement analysis, choreography, and dance education equipment. The paper proposes three areas for reform and innovation in the future integration of AI with dance education: first, optimizing and upgrading dance teaching software and equipment; second, utilizing AI to promote students' autonomous learning; and third, integrating AI into dance teaching evaluation. Additionally, the integration of AI technology will help uncover valuable dance teaching resources, laying the foundation for innovation in dance education.

BackgroundFew existing interactive rehabilitation systems can effectively communicate multiple aspects of movement performance simultaneously, in a manner that appropriately adapts across various training scenarios. In order to address the need for such systems within stroke rehabilitation training, a unified approach for designing interactive systems for upper limb rehabilitation of stroke survivors has been developed and applied for the implementation of an Adaptive Mixed Reality Rehabilitation (AMRR) System.ResultsThe AMRR system provides computational evaluation and multimedia feedback for the upper limb rehabilitation of stroke survivors. A participant's movements are tracked by motion capture technology and evaluated by computational means. The resulting data are used to generate interactive media-based feedback that communicates to the participant detailed, intuitive evaluations of his performance. This article describes how the AMRR system's interactive feedback is designed to address specific movement challenges faced by stroke survivors. Multimedia examples are provided to illustrate each feedback component. Supportive data are provided for three participants of varying impairment levels to demonstrate the system's ability to train both targeted and integrated aspects of movement.ConclusionsThe AMRR system supports training of multiple movement aspects together or in isolation, within adaptable sequences, through cohesive feedback that is based on formalized compositional design principles. From preliminary analysis of the data, we infer that the system's ability to train multiple foci together or in isolation in adaptable sequences, utilizing appropriately designed feedback, can lead to functional improvement. The evaluation and feedback frameworks established within the AMRR system will be applied to the development of a novel home-based system to provide an engaging yet low-cost extension of training for longer periods of time.

IntroductionThe World Health Organization (WHO) acknowledges the presence of a significant body of research on the positive effects of the arts on health, considering a variety of factors including physical well-being, quality of life, and social and community impact. The model that underlies cultural welfare puts the performing arts, visual arts, and cultural heritage at the service of people personal and societal well-being. The potential connections between movements of the body and artistic content have been extensively studied over time, considering movement as a non-verbal language with a universal character.MethodsThis pilot study presents the results of the validation of an innovative multimodal system, the DanzArTe-Emotional Wellbeing Technology, designed to support active and participative experience of older people providing physical and cognitive activation through a full-body physical interaction with a traditional visual work of art of religious subject. DanzArTe supports a replicable treatment protocol for multidimensional frailty, administered through a low cost and scalable technological platform capable of generating real-time visual and auditory feedback (interactive sonification) from the automated analysis of individual as well as joint movement expressive qualities. The study involved 45 participants, 23 of whom participated in the DanzArTe program and 22 who were included in the control group.ResultsThe two groups were similar in terms of age (p = 0.465) and gender (p = 0.683). The results showed that the DanzArTe program had a positive impact on participants' self-perceived psychological health and well-being (Mean Psychological General Well-Being Index—Short T1 = 19.6 ± 4.3 Vs. T2 = 20.8 ± 4.9; p = 0.029). The same trend was not observed in the control group (p = 0.389).DiscussionThe findings suggest that such programs may have a significant impact particularly on the mental and social well-being of older adults and could be a valuable tool for promoting healthy aging and improving quality of life.

With the great changes in the social, economic, and cultural background, the traditional education model can no longer meet the current goal of cultivating dance talents. The application of wearable devices based on deep learning helps to improve students’ understanding and application of dance movements. Through extensive data analysis, experimental research, and kinesthetic theory, it is found that trainers based on deep neural networks can effectively improve students’ overall learning performance. At the same time, the application of emotion-intelligence teaching mode theory in dance experiments and the conclusions drawn from the experimental research fully demonstrate the teaching advantages of applying deep learning wearable devices to dance emotion-intelligence teaching mode. In order to explore the application of wearable devices based on deep learning in dance teaching, this paper discusses the improvement of deep learning through the elaboration of parameter smoothing initialization, convolutional pooling layer, optimal smoothing filter, and dynamic pruning method, and then a wearable device is designed. A device can recognize dance movements to verify the application of deep learning-based wearables in dance teaching in emotional mode.

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

In order to improve the effect of dance teaching action analysis, this paper uses dance action skill feature identification method to analyze dance teaching action and combines teaching and technology to simulate dance action. Moreover, this paper conducts dynamic modeling of dance movements and analyzes the typical properties of several dynamic modeling techniques. At the same time, this paper takes the double-joint dance movement limb as the control object for comparative analysis. The comparison shows that the fractional sliding mode approaching law has better smoothing properties. In addition, this paper selects the control method combining fractional calculus and sliding mode control theory to improve the tracking speed and following effect of the double joints of dance movements. Finally, this paper studies the variance virtual spindle cross-coupling control method based on fractional-order sliding mode to further improve the synchronization accuracy of dance movement limb joints. From the experimental data, it can be seen that the application effect of dance movement skill feature recognition in dance teaching movement analysis is very good.

Dance is an art of human movement that takes professional actions as the main form of expression. With the deep integration of computational technologies in the education field, intelligent dance motion recognition and analysis have become a key direction for promoting the digital transformation of dance education. Aiming at three core challenges in traditional online teaching—difficulty in intuitively capturing precise data of dance movements through videos alone, inability to locate key defects in movement techniques, and lack of interactive feedback and quantitative evaluation of movement quality—this study proposes the POSE-LSTM computational model integrating action recognition algorithms. The model implements posture recognition via the AlphaPose algorithm, transforms spatio-temporal information into action feature vectors, and inputs the normalized features into a pre-trained LSTM neural network for classification encoding. Experiments on the iDance dataset demonstrate that the algorithm achieves an identification accuracy of 0.9825 and a recall rate of 0.9802 for dance movements. Teaching practice verification shows that with the POSE-LSTM model, the action scoring accuracy increases to 98%, the average teaching time per movement is reduced by 23%, and the instructor-directed practice time per movement is decreased by 39%. The study finally discusses the development trends of computational methods in dance education and the prospects of motion recognition systems in engineering applications.