Human Dance Research Articles

Physical strength and gender identification from dance movements

Here we show that gender identification of male (but not female) heterosexual, right-handed dancers correlates with physical strength (measured via handgrip strength) after controlling for the effect of body-mass-index on strength. Using optical motion capture technology, we collected the dance movements of men and women for subsequent animations of uniform shape- and texture-standardized virtual characters (avatars). Short video clips (15s) of these movements were presented to male and female adults and children, who were asked to identify the gender of the avatar. Gender identification performance was significantly higher than chance for both adults and children. Among adults (but not among children) the avatars of male dancers who were physically stronger were perceived as males significantly more often than were the avatars of male dancers who were physically weaker. There was no relationship between strength and gender identification for female dancers. We conclude that physical strength affects gender identification from human dance movements at least for male dancers, and that pre-pubertal children might not be sensitive to strength cues in dance movements.

Perception of social interactions for spatially scrambled biological motion.

It is vitally important for humans to detect living creatures in the environment and to analyze their behavior to facilitate action understanding and high-level social inference. The current study employed naturalistic point-light animations to examine the ability of human observers to spontaneously identify and discriminate socially interactive behaviors between two human agents. Specifically, we investigated the importance of global body form, intrinsic joint movements, extrinsic whole-body movements, and critically, the congruency between intrinsic and extrinsic motions. Motion congruency is hypothesized to be particularly important because of the constraint it imposes on naturalistic action due to the inherent causal relationship between limb movements and whole body motion. Using a free response paradigm in Experiment 1, we discovered that many naïve observers (55%) spontaneously attributed animate and/or social traits to spatially-scrambled displays of interpersonal interaction. Total stimulus motion energy was strongly correlated with the likelihood that an observer would attribute animate/social traits, as opposed to physical/mechanical traits, to the scrambled dot stimuli. In Experiment 2, we found that participants could identify interactions between spatially-scrambled displays of human dance as long as congruency was maintained between intrinsic/extrinsic movements. Violating the motion congruency constraint resulted in chance discrimination performance for the spatially-scrambled displays. Finally, Experiment 3 showed that scrambled point-light dancing animations violating this constraint were also rated as significantly less interactive than animations with congruent intrinsic/extrinsic motion. These results demonstrate the importance of intrinsic/extrinsic motion congruency for biological motion analysis, and support a theoretical framework in which early visual filters help to detect animate agents in the environment based on several fundamental constraints. Only after satisfying these basic constraints could stimuli be evaluated for high-level social content. In this way, we posit that perceptual animacy may serve as a gateway to higher-level processes that support action understanding and social inference.

René Descartes: Dancing and Mustering Substances

This article addresses two of the fundamental issues present in the philosophy of Rene Descartes (1596--1650): (i) his division into thinking and spatially extended substances, together with both substantive and methodological aspects of his understanding of their character as a unity, and (ii) his conception of the human body as a machine. I shall illustrate these topics here using the example of Descartes' own military training in the army of the Prince Maurice of Nassau and, as a contrast to this, also his work in another area of highly trained human activity - namely, human dance. In speaking about dance I will not differentiate between its diverse types (individual, dancing with a partner, group dances, ritual, folk, Latin American, typical or non-typical style, mixed, etc.). I am only interested in dance as a form of continuous, rule-governed spatial movement by human subjects. I also have pretty much the same thing in mind when speaking about military training, that is, a form of continuous, rule-governed spatial movement on the part of its protagonists.

Toward a Dancing Robot With Listening Capability: Keypose-Based Integration of Lower-, Middle-, and Upper-Body Motions for Varying Music Tempos

This paper presents the development toward a dancing robot that can listen to and dance along with musical performances. One of the key components of this robot is the ability to modify its dance motions with varying tempos, without exceeding motor limitations, in the same way that human dancers modify their motions. In this paper, we first observe human performances with varying musical tempos of the same musical piece, and then analyze human modification strategies. The analysis is conducted in terms of three body components: lower, middle, and upper bodies. We assume that these body components have different purposes and different modification strategies, respectively, for the performance of a dance. For all of the motions of these three components, we have found that certain fixed postures, which we call keyposes, tend to be preserved. Thus, this paper presents a method to create motions for robots at a certain music tempo, from human motion at an original music tempo, by using these keyposes. We have implemented these algorithms as an automatic process and validated their effectiveness by using a physical humanoid robot HRP-2. This robot succeeded in performing the Aizu-bandaisan dance, one of the Japanese traditional folk dances, 1.2 and 1.5 times faster than the tempo originally learned, while maintaining its physical constraints. Although we are not achieving a dancing robot which autonomously interacts with varying music tempos, we think that our method has a vital role in the dancing-to-music capability.

Adaptive human–robot interaction in sensorimotor task instruction: From human to robot dance tutors

We explore the potential for humanoid robots to interact with children in a dance activity. In this context, the robot plays the role of an instructor to guide the child through several dance moves to learn a dance phrase. We participated in 30 dance sessions in schools to study human–human interaction between children and a human dance teacher, and to identify the applied methodologies. Based on the strategies observed, both social and task-dependent, we implemented a robotic system capable of autonomously instructing dance sequences to children while displaying basic social cues to engage the child in the task. Experiments were performed in a hospital with the Nao robot interacting with 12 children through multiple encounters, when possible (18 sessions, 236 min). Observational analysis through video recordings and survey evaluations were used to assess the quality of interaction. Moreover, we introduce an involvement measure based on the aggregation of observed behavioral cues to assess the level of interest in the interaction through time. The analysis revealed high levels of involvement, while highlighting the need for further research into social engagement and adaptation with robots over repeated sessions.

Recognizing Induced Emotions of Happiness and Sadness from Dance Movement

Recent research revealed that emotional content can be successfully decoded from human dance movement. Most previous studies made use of videos of actors or dancers portraying emotions through choreography. The current study applies emotion induction techniques and free movement in order to examine the recognition of emotional content from dance. Observers (N = 30) watched a set of silent videos showing depersonalized avatars of dancers moving to an emotionally neutral musical stimulus after emotions of either sadness or happiness had been induced. Each of the video clips consisted of two dance performances which were presented side-by-side and were played simultaneously; one of a dancer in the happy condition and one of the same individual in the sad condition. After every film clip, the observers were asked to make forced-choices concerning the emotional state of the dancer. Results revealed that observers were able to identify the emotional state of the dancers with a high degree of accuracy. Moreover, emotions were more often recognized for female dancers than for their male counterparts. In addition, the results of eye tracking measurements unveiled that observers primarily focus on movements of the chest when decoding emotional information from dance movement. The findings of our study show that not merely portrayed emotions, but also induced emotions can be successfully recognized from free dance movement.

The Impact of the Bass Drum on Human Dance Movement

The present study aims to gain better insight into the connection between music and dance by examining the dynamic effects of the bass drum on a dancing audience in a club-like environment. One hundred adult participants moved freely in groups of five to a musical sequence that comprised six songs. Each song consisted of one section that was repeated three times, each time with a different sound pressure level of the bass drum. Hip and head movements were recorded using motion capture and motion sensing. The study demonstrates that people modify their bodily behavior according to the dynamic level of the bass drum when moving to contemporary dance music in a social context. Participants moved more actively and displayed a higher degree of tempo entrainment as the sound pressure level of the bass drum increased. These results indicate that the prominence of the bass drum in contemporary dance music serves not merely as a stylistic element; indeed, it has a strong influence on dancing itself.

2P1-B04 人のダンスモーションデータの多様な足の接地状態を保存するヒューマノイド動作生成法(ヒューマノイド)

We developed a generation system of a humanoid dance motion reserving three dance important elements : foot touch state, joint position, and Key-pose. First, the system recognizes 7 kinds of foot touch states in human dance motion using the height and velocity of ankles, the yaw angle velocity and pitch angle of feet, and the trajectory of ZMP. Second, the system calculates humanoid's postures from human's reserving joint positions by using the regression plane of the crotch, knee, ankle, and toe position of each leg. Finally, the trajectory ,which is the sequence of the humanoid's postures, is modified not only to satisfy the physical restriction but also to reserve features of each foot touch state and Key-pose.

Local phase quantization descriptor for improving shape retrieval/classification

Shape classification is a field of study with applications ranging from object classification to leaf recognition. In this paper we present an approach based on a matrix descriptor, the local phase quantization, for improving the performance of such widely used shape descriptors as the inner distance shape context (ID), shaper context (SC), and height functions (HF). The basic idea of our novel approach is to transform the shape descriptor obtained by ID/SC/HF into a matrix so that matrix descriptors can be extracted. These matrix descriptors are then compared with the Jeffrey distance and combined with standard ID/SC/HF shape similarity. Since it has recently been shown that ID/SC/HF shape similarities can be coupled with learning context-sensitive shape similarity using graph transduction (LGT) for improving the results, we have also coupled our approach with LGT. Our proposed approach is tested on a wide variety of shape databases including MPEG7 CE-Shape-1, Kimia silhouettes, Tari dataset, a leaf dataset, a tools dataset, a myths figures dataset, and our new human dancer dataset. The experimental results demonstrate that the proposed approach yields significant improvements over baseline shape matching algorithms. All Matlab code used in the proposed paper is available at bias.csr.unibo.it/nanni/Shape.rar.

Example-Based Automatic Music-Driven Conventional Dance Motion Synthesis

We introduce a novel method for synthesizing dance motions that follow the emotions and contents of a piece of music. Our method employs a learning-based approach to model the music to motion mapping relationship embodied in example dance motions along with those motions' accompanying background music. A key step in our method is to train a music to motion matching quality rating function through learning the music to motion mapping relationship exhibited in synchronized music and dance motion data, which were captured from professional human dance performance. To generate an optimal sequence of dance motion segments to match with a piece of music, we introduce a constraint-based dynamic programming procedure. This procedure considers both music to motion matching quality and visual smoothness of a resultant dance motion sequence. We also introduce a two-way evaluation strategy, coupled with a GPU-based implementation, through which we can execute the dynamic programming process in parallel, resulting in significant speedup. To evaluate the effectiveness of our method, we quantitatively compare the dance motions synthesized by our method with motion synthesis results by several peer methods using the motions captured from professional human dancers' performance as the gold standard. We also conducted several medium-scale user studies to explore how perceptually our dance motion synthesis method can outperform existing methods in synthesizing dance motions to match with a piece of music. These user studies produced very positive results on our music-driven dance motion synthesis experiments for several Asian dance genres, confirming the advantages of our method.

Time and output warping of control systems: Comparing and imitating motions

The problem of making one system “mimic” a motion generated by another is studied. To address this, an optimal tracking problem is introduced that, in addition to tracking, optimizes over functions that deform or “warp” the time axis and the output space. Parametric and nonparametric versions of the time-warped tracking problem are presented and reduced to standard Bolza problems. The output warping problem is treated for affine and piecewise affine output warping functions. Example applications are given, including that of controlling a marionette to mimic a human dancer.

Male dance moves that catch a woman's eye.

Male movements serve as courtship signals in many animal species, and may honestly reflect the genotypic and/or phenotypic quality of the individual. Attractive human dance moves, particularly those of males, have been reported to show associations with measures of physical strength, prenatal androgenization and symmetry. Here we use advanced three-dimensional motion-capture technology to identify possible biomechanical differences between women's perceptions of 'good' and 'bad' male dancers. Nineteen males were recorded using the 'Vicon' motion-capture system while dancing to a basic rhythm; controlled stimuli in the form of avatars were then created in the form of 15 s video clips, and rated by 39 females for dance quality. Initial analyses showed that 11 movement variables were significantly positively correlated with perceived dance quality. Linear regression subsequently revealed that three movement measures were key predictors of dance quality; these were variability and amplitude of movements of the neck and trunk, and speed of movements of the right knee. In summary, we have identified specific movements within men's dance that influence women's perceptions of dancing ability. We suggest that such movements may form honest signals of male quality in terms of health, vigour or strength, though this remains to be confirmed.

Progress towards intelligent support for human articulation of concepts from examples

Within this article we present a progress plan and the motivation of the research steps involved within the proposed learning framework. In a technological century, we aim to create a guided learning technology. We aim to use machine learning to support young students to articulate concepts from examples, in particular form images. Therefore, we address here the learning of natural concepts and principles of working memories that can support the learning activities. We focus also on the learning within a visual environment. We analyse then the potential of machine learning techniques to guide the human learning from examples. For this purpose, we propose a framework based on using machine learning to reason from student classifications, which we call HMCD (Human Machine Concept Dance).

Preface

Dance is ubiquitous among humans and has received attention from several disciplines. Ethnographic documentation suggests that dance has a signaling function in social interaction. It can influence mate preferences and facilitate social bonds. Research has provided insights into the proximate mechanisms of dance, individually or when dancing with partners or in groups. Here, we review dance research from an evolutionary perspective. We propose that human dance evolved from ordinary (non-communicative) movements to communicate socially relevant information accurately. The need for accurate social signaling may have accompanied increases in group size and population density. Because of its complexity in production and display, dance may have evolved as a vehicle for expressing social and cultural information. Mating-related qualities and motives may have been the predominant information derived from individual dance movements, whereas group dance offers the opportunity for the exchange of socially relevant content, for coordinating actions among group members, for signaling coalitional strength, and for stabilizing group structures. We conclude that, despite the cultural diversity in dance movements and contexts, the primary communicative functions of dance may be the same across societies.

Spontaneous Motor Entrainment to Music in Multiple Vocal Mimicking Species

The human capacity for music consists of certain core phenomena, including the tendency to entrain, or align movement, to an external auditory pulse [1-3]. This ability, fundamental both for music production and for coordinated dance, has been repeatedly highlighted as uniquely human [4-11]. However, it has recently been hypothesized that entrainment evolved as a by-product of vocal mimicry, generating the strong prediction that only vocal mimicking animals may be able to entrain [12, 13]. Here we provide comparative data demonstrating the existence of two proficient vocal mimicking nonhuman animals (parrots) that entrain to music, spontaneously producing synchronized movements resembling human dance. We also provide an extensive comparative data set from a global video database systematically analyzed for evidence of entrainment in hundreds of species both capable and incapable of vocal mimicry. Despite the higher representation of vocal nonmimics in the database and comparable exposure of mimics and nonmimics to humans and music, only vocal mimics showed evidence of entrainment. We conclude that entrainment is not unique to humans and that the distribution of entrainment across species supports the hypothesis that entrainment evolved as a by-product of selection for vocal mimicry.

Unspoken knowledge: Implicit learning of structured human dance movement.

The sequencing of dance movements may be thought of as a grammar. We investigate implicit learning of regularities that govern sequences of unfamiliar, discrete dance movements. It was hypothesized that observers without prior experience with contemporary dance would be able to learn regularities that underpin structured human movement. Thirty-one adults were assigned to either an exposure or a control group. Exposure consisted of 22 grammatical 3-, 4-, and 5-movement sequences presented twice in random order; sequence duration ranged from 9 to 19 s. In a test phase, exposure and control groups identified previously unseen sequences as grammatical or ungrammatical, and rated confidence of judgment. The exposure group selected significantly more new grammatical sequences in the test phase than the control group. In addition, for the exposure group, the zero correlation criterion, wherein no relation between confidence and accuracy indicates unconscious knowledge, was satisfied. Through exposure, novice observers can learn a grammar that governs the sequencing of dance movements. This has implications for implicit learning of long sequences, working memory, and the development of expectations through exposure to contemporary dance.

A self-organising multiple model architecture for motor imitation

Learning by imitation allows humans to easily transfer motor knowledge between individuals. Our research is aimed towards equipping robots with imitative capabilities, so humans can simply show a robot what to do. This will greatly simplify how humans program robots. To achieve imitative behaviour, we have implemented a self-organising connectionist modular architecture on a simulated robot. Motion tracking was used to gather data of human dance movements. When imitating the dance movements, the architecture self-organises the decomposition of movements into submovements, which are controlled by different modules. The modules both collaborate and compete for control during the movement. The trajectory recorded during motion tracking was repeated, revealing recurrent neural activation patterns of the inverse models (i.e., controllers), indicating that the modules specialise on specific parts of the trajectory.

Synthesis of Dance Performance Based on Analyses of Human Motion and Music

Recent progress in robotics has a great potential, and we are considering to develop a dancing humanoid robot for entertainment robots. In this paper, we propose three fundamental methods of a dancing robot aimed at the sound feedback system in which a robot listens to music and automatically synthesizes dance motion based on the musical features. The first study analyzes the relationship between motion and musical rhythm and extracts important features for imitating human dance motion. The second study models modification of upper body motion based on the speed of played music so that a humanoid robot dances to a faster musical speed. The third study automatically synthesizes dance performance that reflects both rhythm and mood of input music.

Learning from Observation Paradigm: Leg Task Models for Enabling a Biped Humanoid Robot to Imitate Human Dances

This paper proposes a framework that achieves the Learning from Observation paradigm for learning dance motions. The framework enables a humanoid robot to imitate dance motions captured from human demonstrations. This study especially focuses on leg motions to achieve a novel attempt in which a biped-type robot imitates not only upper body motions but also leg motions including steps. Body differences between the robot and the original dancer make the problem difficult because the differences prevent the robot from straightforwardly following the original motions and they also change dynamic body balance. We propose leg task models, which play a key role in solving the problem. Low-level tasks in leg motion are modelled so that they clearly provide essential information required for keeping dynamic stability and important motion characteristics. The models divide the problem of adapting motions into the problem of recognizing a sequence of the tasks and the problem of executing the task sequence. We have developed a method for recognizing the tasks from captured motion data and a method for generating the motions of the tasks that can be executed by existing robots including HRP-2. HRP-2 successfully performed the generated motions, which imitated a traditional folk dance performed by human dancers.

A preliminary investigation of the associations between digit ratio and women’s perception of men’s dance

Recent research has revealed that variation in human dancing ability is related to levels of fluctuating asymmetry, and that women rate symmetrical male dancers more positively. We measured the lengths of the 2nd (index) and 4th (ring) finger in a sample of young men and recorded short digital video clips of their dance movements. A panel of 104 female judges rated 12 clips of men with the lowest and highest finger-length ratios (2D:4D) for attractiveness, dominance, and masculinity. We found that dances by men with low (masculinised) 2D:4D ratios were rated significantly higher on attractiveness, dominance, and masculinity than dancers with high (feminised) 2D:4D. There were no significant differences between the two groups of dancers for age and other physical measures such as waist-to-hip ratio (WHR) and body-mass-index (BMI). Since there is evidence that finger-length ratios negatively correlate with testosterone exposure in utero, male dancing abilities may be organized early during development. Moreover, women’s ability to perceive differences in dance movement of men with low and high 2D:4D may indicate that dance provides some cues to phenotypic condition, relevant for sexual selection.