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"Dance Data processing."
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Fine‐Grained Dance Style Classification Using an Optimized Hybrid Convolutional Neural Network Architecture for Video Processing Over Multimedia Networks
Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing video analysis in training offers substantial advantages, including the potential to train future dancers using innovative technologies. Over time, intricate dance gestures can be honed, reducing the burden on instructors who would, otherwise, need to provide repetitive demonstrations. Recognizing dancers’ movements, evaluating and adjusting their gestures, and extracting cognitive functions for efficient evaluation and classification are pivotal aspects of our model. Deep learning currently stands as one of the most effective approaches for achieving these objectives, particularly with short video clips. However, limited research has focused on automated analysis of dance videos for training purposes and assisting instructors. In addition, assessing the quality and accuracy of performance video recordings presents a complex challenge, especially when judges cannot fully focus on the on‐stage performance. This paper proposes an alternative to manual evaluation through a video‐based approach for dance assessment. By utilizing short video clips, we conduct dance analysis employing techniques such as fine‐grained dance style classification in video frames, convolutional neural networks (CNNs) with channel attention mechanisms (CAMs), and autoencoders (AEs). These methods enable accurate evaluation and data gathering, leading to precise conclusions. Furthermore, utilizing cloud space for real‐time processing of video frames is essential for timely analysis of dance styles, enhancing the efficiency of information processing. Experimental results demonstrate the effectiveness of our evaluation method in terms of accuracy and F1‐score calculation, with accuracy exceeding 97.24% and the F1‐score reaching 97.30%. These findings corroborate the efficacy and precision of our approach in dance evaluation analysis.
Journal Article
Dance training is superior to repetitive physical exercise in inducing brain plasticity in the elderly
Animal research indicates that a combination of physical activity and sensory enrichment has the largest and the only sustaining effect on adult neuroplasticity. Dancing has been suggested as a human homologue to this combined intervention as it poses demands on both physical and cognitive functions. For the present exploratory study, we designed an especially challenging dance program in which our elderly participants constantly had to learn novel and increasingly difficult choreographies. This six-month-long program was compared to conventional fitness training matched for intensity. An extensive pre/post-assessment was performed on the 38 participants (63-80 y), covering general cognition, attention, memory, postural and cardio-respiratory performance, neurotrophic factors and-most crucially-structural MRI using an exploratory analysis. For analysis of MRI data, a new method of voxel-based morphometry (VBM) designed specifically for pairwise longitudinal group comparisons was employed. Both interventions increased physical fitness to the same extent. Pronounced differences were seen in the effects on brain volumes: Dancing compared to conventional fitness activity led to larger volume increases in more brain areas, including the cingulate cortex, insula, corpus callosum and sensorimotor cortex. Only dancing was associated with an increase in plasma BDNF levels. Regarding cognition, both groups improved in attention and spatial memory, but no significant group differences emerged. The latter finding may indicate that cognitive benefits may develop later and after structural brain changes have taken place. The present results recommend our challenging dance program as an effective measure to counteract detrimental effects of aging on the brain.
Journal Article
Older adults’ acceptance of a robot for partner dance-based exercise
Partner dance has been shown to be beneficial for the health of older adults. Robots could potentially facilitate healthy aging by engaging older adults in partner dance-based exercise. However, partner dance involves physical contact between the dancers, and older adults would need to be accepting of partner dancing with a robot. Using methods from the technology acceptance literature, we conducted a study with 16 healthy older adults to investigate their acceptance of robots for partner dance-based exercise. Participants successfully led a human-scale wheeled robot with arms (i.e., a mobile manipulator) in a simple, which we refer to as the Partnered Stepping Task (PST). Participants led the robot by maintaining physical contact and applying forces to the robot's end effectors. According to questionnaires, participants were generally accepting of the robot for partner dance-based exercise, tending to perceive it as useful, easy to use, and enjoyable. Participants tended to perceive the robot as easier to use after performing the PST with it. Through a qualitative data analysis of structured interview data, we also identified facilitators and barriers to acceptance of robots for partner dance-based exercise. Throughout the study, our robot used admittance control to successfully dance with older adults, demonstrating the feasibility of this method. Overall, our results suggest that robots could successfully engage older adults in partner dance-based exercise.
Journal Article
Let’s all dance: Enhancing amateur dance motions
Professional dance is characterized by high impulsiveness, elegance, and aesthetic beauty. In order to reach the desired professionalism, it requires years of long and exhausting practice, good physical condition, musicality, but also, a good understanding of choreography. Capturing dance motions and transferring them to digital avatars is commonly used in the film and entertainment industries. However, so far, access to high-quality dance data is very limited, mainly due to the many practical difficulties in capturing the movements of dancers, making it prohibitive for large-scale data acquisition. In this paper, we present a model that enhances the professionalism of amateur dance movements, allowing movement quality to be improved in both spatial and temporal domains. Our model consists of a
dance-to-music alignment
stage responsible for learning the optimal temporal alignment path between dance and music, and a
dance-enhancement
stage that injects features of professionalism in both spatial and temporal domains. To learn a homogeneous distribution and credible mapping between the heterogeneous professional and amateur datasets, we generate amateur data from professional dances taken from the AIST++ dataset. We demonstrate the effectiveness of our method by comparing it with two baseline motion transfer methods via thorough qualitative visual controls, quantitative metrics, and a perceptual study. We also provide temporal and spatial module analysis to examine the mechanisms and necessity of key components of our framework.
Journal Article
QEAN: quaternion-enhanced attention network for visual dance generation
The study of music-generated dance is a novel and challenging image generation task. It aims to input a piece of music and seed motions, then generate natural dance movements for the subsequent music. Transformer-based methods face challenges in time-series prediction tasks related to human movements and music due to their struggle in capturing the nonlinear relationship and temporal aspects. This can lead to issues like joint deformation, role deviation, floating, and inconsistencies in dance movements generated in response to the music. In this paper, we propose a quaternion-enhanced attention network for visual dance synthesis from a quaternion perspective, which consists of a spin position embedding (SPE) module and a quaternion rotary attention (QRA) module. First, SPE embeds position information into self-attention in a rotational manner, leading to better learning of features of movement sequences and audio sequences and improved understanding of the connection between music and dance. Second, QRA represents and fuses 3D motion features and audio features in the form of a series of quaternions, enabling the model to better learn the temporal coordination of music and dance under the complex temporal cycle conditions of dance generation. Finally, we conducted experiments on the dataset AIST++, and the results show that our approach achieves better and more robust performance in generating accurate, high-quality dance movements. Our source code and dataset can be available from
https://github.com/MarasyZZ/QEAN
and
https://google.github.io/aistplusplus_dataset
, respectively.
Journal Article
How partnering changes the process of postural control?
The aim of the study was to identify changes in the mechanism of postural control among ballroom dancers between standing solo and standing with a partner during specific standard dance positions. Specifically, the study attempted to determine whether the male partner plays a stabilising role in the dance couple. A total of seven competitive dance couples participated in the study. The experimental procedure comprised four dance positions characteristic of international standard dances: standard, starting, chasse and contra check. The dance positions were staged twice – while standing solo and while standing with a partner. The assumption of the assessed position was preceded by a dance phase after which the participants were instructed to freeze on a force plate and hold the position for 30 s. To examine whether subjects standing solo or with partners had greater rambling (RM) or trembling (TR) components in their dance postural profile, the ratios of RM to the center of foot pressure (COP) and TR to COP were computed for velocity. No significant differences were observed in the velocity of COP between standing solo and standing with a partner (p > 0.05). However, during the standard and starting positions, female and male dancers standing solo were characterised by higher values of the velocity of RM/COP ratio and lower values of the velocity of TR/COP ratio than those standing with a partner (p < 0.05). According to the theory behind the RM and TR decomposition, an increase in TR components could indicate a higher reliance on spinal reflexes, which would suggest greater automaticity.
Journal Article
DANCE: a deep learning library and benchmark platform for single-cell analysis
DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
Journal Article
Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology
Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.
Journal Article
Test-retest reliability of a dance-specific jump test using wearable technology among university contemporary dancers
•The reliability of university contemporary dancers’ landing asymmetries in a dance-specific jump test was poor using IMUs.•Narrow limits of agreement in the assessment of flight time and jump height was acceptable using IMUs.•Accelerometers placed on the lower limb can be used to quantify jump metrics during training or rehabilitation in dancers.
A dance-specific jump test utilizing inertial measurement units (IMUs) to determine limb symmetry indices (LSI) may prove useful for clinicians responsible for dancer populations.
The aim of this study was to determine test–retest reliability of a dance-specific jump test using wearable technology to capture limb asymmetry in university contemporary dancers. Nineteen female undergraduate dance majors participated in two testing sessions one week apart. IMUs (Vicon Motion Systems, UK) were attached to the distal anteromedial aspect of each tibia and at the L5/S1 joint. Participants performed three jumps: countermovement jump (CMJ), a single leg side hop (SH), and a dance-specific jump (DSJ). Test-retest reliability of asymmetry measures [i.e., peak impact acceleration (g), mean asymmetry (%)] and performance measures [i.e., flight time (sec), time to completion (sec), and jump height (cm)] were estimated using intraclass correlation coefficients [ICCs (95% CI)] and Bland Altman methods of agreement [95% limits of agreement (LOA)]. For asymmetry measures, reliability was poor for CMJ [ICC 0.36 (95% CI −0.08–0.77)], moderate for SH [ICC 0.66 (95% CI 0.35–0.88)] and good for DSJ [ICC 0.82 (95% CI 0.49–0.94)]. Wide 95% LOA were demonstrated for all jumps. For performance measures, moderate to good reliability and acceptable LOA were found for all jumps. While a lack of agreement was found for asymmetry measures, sufficient reliability and acceptable agreement were established for performance measures for all jump tests in university contemporary dancers. This method may be used to monitor the performance of jumps and jump load throughout a training season.
Journal Article