Observation Scheme for Interaction with Embodied ...

Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics Zhuhai, China, December 6-9, 2015

Observation Scheme for Interaction with Embodied Intelligent Agents based on Laban Notation Emilia I. Barakova, Roos van Berkel, Liang Hiah, Yu-Fang Teh, Ceil Werts



Abstract—Designing more intuitive interaction with robots and other intelligent agents relies on knowledge gathered through measurement methods that offer a limited perspective, such as questionnaires and naïve observers. To broaden the toolset for designers of interactive robots, we developed an observation scheme based on the framework of Laban/Bartenieff Movement Analysis. Instead of limiting the evaluation to certified movement analysts, we have set and translated parameters that can be evaluated by an uncertified observer. The proposed observation scheme aims to provide more objective measures for the unconscious interaction patterns so designers can use it alongside, or instead of self-report, physiological or other measurements. The development was carried out in the context of a study about participants’ interactions with an expressive walk-in closet that was able to take on a more dominant or submissive attitude by using dynamic lighting behaviors. Two certified movement analysts (CMA’s) determined and translated the main categories of the Laban framework to measurable qualities for the particular experimental setting. The outcome was validated by measuring the inter-rater agreement and the evaluation of 3 CMA’s, who evaluated the videos from the experiment. The observation scheme is not explicitly validated with non-certified LMA observers yet, however, the set-up is such that the observer does not require specific prior knowledge on Laban/Bartenieff Movement Analysis.

Keywords: Measuring emotions and nonverbal interaction; Affective HCI. Laban/Bartenieff Movement Analysis, Human-Robot Interaction; Ubiquitous and Context-Aware Computing. I. INTRODUCTION The fields of human-robot interaction and ambient intelligence are based upon a trend in engineering and design to create mechatronic systems that can sense the context and the behavioral cues of their users and adapt to them in order to guarantee a seamless and optimal user experience [1]. Often, the paradigm that is used for designing the behavior of such systems is one that mimics interpersonal interaction [2]. Digital and electronic systems and especially robots are often personified by means of ascribing human attributes to them. For example, a system can be described as “friendly” or “smart” [3] or “dominant” or “submissive” [4][23]. Under the tendency The authors are with the Department of Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands (e-mail:[email protected]; [email protected]; [email protected])..

978-1-4673-9675-2/15/$31.00 © 2015 IEEE

to personify digital systems lies the assumption that such systems may, to a certain extent, function and communicate more efficiently with humans by acting like humans would. This personification will require that we need to measure the human-like qualities such as expressiveness. Measuring expressiveness, emotions, mental states and attitudes is a challenging task [5][6][7][8]. Most of the approaches rely on questionnaires and self-reports. Some attempts to develop more objective measures have been made by Tetteroo at al. [9] who proposed a measure for expressiveness of movement in game play, while other researchers [10][11] used components from Laban/Bartenieff Movement Analysis (LMA) framework for that purpose. LMA is one of the most acclaimed methods for movement analysis. Observing and describing movement according to predefined categories, it aims to systematically deconstruct a movement scenario in order to translate a subjective interpretation to a more objective perspective. The analysis departs from the notion of 'whole-part-whole': after observing the entire movement score of the experiment, the observation parameters are determined. Consequently the observer analyzes the same score from different perspectives. These perspectives range from the use of (specific parts of) the body, how the mover engages with space, the way in which the movements are ‘performed’ and how the surrounding space affects the mover’s body. The combination of predefined questions and the systematic deconstruction of the movement score enable the observers to reflect on the findings in relation to the initial research question. This method has consequently not only been used in dance and choreography, but also in acting, physical therapy, and somatic movement practices. Besides being applied to the practices named above, LMA is also used increasingly as an interdisciplinary tool in contexts of academic research that seemingly do not relate directly to movement or dance. There are many examples of interdisciplinary studies such as animal movement behavior [12][13], psychological disorders [15], visually guided reaching by babies and stroke patients [14] [16], and lately in robotics and human-system interaction [8][10][11]. The Laban/Bartenieff framework distinguishes itself from other movement notation systems by its potential to encode the expressiveness of the movement. A disadvantage of this coding system is that 2525

the deconstruction of the movement is so complex, that it requires detailed and repeated observation, and these observations need to be performed by certified experts. Even certified analysts can differ in opinions when analyzing the movement [8]. To overcome these difficulties and utilize on the usefulness of this analysis we propose a way to make LMA more quantifiable and accessible to a broader audience. Within the current research, participants that interacted with an intelligent closet have been interviewed with regards to their experience with the different agent behaviors. These interviews are of course subjective. We aim to connect the outcomes from such subjective questionnaires to a more objective tool to measure the experience that different behaviors provoke. In this context, LMA is therefore deliberately used to stimulate perceptual objectivity whilst analyzing human interaction with expressive or intelligent embodied objects. The outcomes from the interviews needed to be compared with the observations of certified movement analysts. In Lourens et al. [8] the expression of several emotions through movement were first clustered based on Laban categories by a learning algorithm and then the same expressions were evaluated by Certified Movement Analysts (CMA’s). This study showed that different CMA’s, trained to observe movement within the same Laban framework, may have different opinions on the expressed emotions or mental states because the used parameters gave too much room for own interpretation. To avoid such an artefact, we created a coding sheet which will give a unified choice of the categories the CMA’s should look at. Movement observation and analysis are often performed with the use of coding sheets. By formulating measurements with specific parameters that were tested during pilot studies, this analysis is framed according to the context of the walk-in closet with pre-designed dynamic lighting behaviors. When observing movement, the observer is required to look at a single movement or a series of movements repeatedly. Within experiments of this kind, video documentation plays a key role to ensure the possibility for repeated movement observation. During this experiment, we filmed the participants while executing their movement task. These videos have been analyzed by three certified LMA professionals. The Laban/Bartenieff categories Body, Effort and Space were identified as relative categories for this experimental setting. While creating the movement coding sheet, the following parameters have been used as points of departure: body actions, movement phrasing, use of surrounding space and degree of involvement with regards to specific task orientation. The paper is organized as follows: Section II gives a description of the context for which the LMA observation scheme was developed. Section III features the points of departure for the development of the questionnaire. Section IV provides a validation of the usage of the

questionnaire, while Sections V and VI offer conclusions and discussion of the contributions we made. II. BACKGROUND INFORMATION AND METHODS A. The context of development of the observation scheme The intelligent walk-in closet was created to explore the interactions between a human and an intelligent object that can express behaviors based on learning [17]. The closet was situated in a sleeping room of the former “Context lab” of the department of Industrial Design, Eindhoven University of Technology, where the participants had sufficient space to move as they would do in a room. The closet was equipped with sensors and a lighting system capable of responding to a user’s movement by changing its dynamic lighting behavior. The participants were situated in the context of the walk-in-closet and asked to perform different scenarios. In each case, the closet would adopt a different style of dynamic lighting behavior.

Fig. 1. Walk-in closet is designed with ceiling spots to lighten the overall environment, shelf lights, used to express the system’s behavior. Pressure sensors under the carpet (orange dashed arrow) track the user’s walking patterns, whereas infrared sensors (red arrows) record hand movements near each shelf.

Behaviors are in essence a sequence of actions with instrumental and expressive quality. In terms of lighting patterns, a behavior may be simulated through the illusion of motion achieved by lighting neighboring lights in rapid succession (below 200ms) [18]. We used apparent motion to simulate behaviors. The design of the lighting behaviors was based on the use of cues that attempts to impose a feeling of social inequality as dominance or submissiveness. According to Schwartz et al. [20], dominant and submissive cues are analogous to being in the fore- or background with regard to space and time. Spatially, this opposition refers to objects (or characters) in the foreground in a representation being regarded as more visible. Tiedens and Fragale [21] associate dominance and submissiveness with expansion and contraction of a body. Temporally, the submissiveness and dominance opposition refers to agents who are leading the interaction to be perceived as more relevant than those 2526

relegated to a second or following role. In the lighting behaviors we designed, the spatial opposition foregroundbackground equates to brighter-dimmer, and the temporal opposition equates to a light that is guiding, in contrast to a light that follows. The neutral control condition was designed so that the lighting behaviors of the closet were in static state and medium intensity. The best evaluated dominant, submissive, and neutral behavior were selected from a variety of behaviors created for this experiment. More detail on the creation and the selection of the behaviors is discussed in [4]. B. Participants Over the course of 4 pilot studies, two Certified Laban/Bartenieff Movement Analysts (CMA’s), six naive observers participated in the development and pre-testing of a coding sheet in order to categorize and assess specific non-verbal movement parameters. To test and challenge observer’s agreement of the 4th (final) pilot, 3 independent CMA’s were recruited to complete the movement coding sheet for the 16 participants in the human-closet interaction test. The video recordings of the other 3 participants were used for the development of the coding sheet. The questions on the coding sheet were discussed with the 3 observers before the observation to take place in order to secure a common point of departure; however the video data analysis was performed independently from one another in separate spaces. C. Measurements based on self-reports To verify the outcomes of the Laban analysis of the behavior of the participants, we used the Self-Assessment Manikin (SAM) [5]. The self-assessment scores ranged from 1 (submissive, not aroused, or sad) to 9 (dominant, aroused, and happy). In addition the Social Dominance Orientation (SDO) questionnaire [19] was used to determine the dominant or submissive tendency in the personality of every participant. D. LMA as a descriptive language to perform movement analysis. This study aims to use LMA as a descriptive method to codify specific movement parameters based on postures and gestures. We have aimed to perceive and interpret non-verbal behavior for the purpose of finding possible relationships between the designed lighting behaviors and movement behaviors. The interpretation of behavior through LMA has been based on quantitative (kinespheric) and qualitative (dynamospheric) characteristics of the movement in real time. In this paper the focus is on how LMA strives for capturing the kinematic (kinespheric) as well as the non-kinematic (dynamospheric) features of movement. The kinematic features are postural and gestural body actions in the reach space of the body. Nonkinematic features of movements are the qualitative aspects of movement, characterized by changes in intensity with regards to the engagement of body mass, the

timing of the movement, the spatial focus on the environment and the continuity of the movement. These changes always accompany (or even direct) the spatialtemporal body relations, therefore the kinematic and nonkinematic features should be observed and analyzed in relation to each other. E. The movement coding sheet The development of the movement coding sheet went through 4 phases, referred to as pilots. In order to develop the coding sheet appropriately, each pilot involved movement analysis of the video material of one participant who interacts within all 3 designed lighting settings. We used 3 of the total amount of 19 videos for the development of the coding sheet. The remaining 16 videos were used for the final pilot. Each video recording contained 3 behaviors of the participants interacting with the neutral, dominant and submissively behaving closet. Two pilots were developed and tested with CMA’s and one tested with 6 professionals with design background. III. A.

DEVELOPMENT OF THE MOVEMENT CODING SHEET Points of Departure

During this study, LMA has been used to analyze and codify specific movement parameters in the context of interacting with an expressive walk-in closet. The points of departure have been determined during the pilot tests. They are all based on what LMA defines as the Basic Body Actions, and have been selected based upon the findings during the trials, i.e. which observations we considered to be most valuable for the experiment. The following points of departure were used: 1) Body (Body Actions): a. Weight shifts from one leg to another b. Gestures specifically related to the contextspecific interaction. c. Postures specifically related to the contextspecific interaction. 2) Gestural & postural involvement: a. Gestural (reaching for clothing without necessarily taking it out), b. Postural (looking at the closet without reaching for items of clothing). 3) Space (Spatial Complexity): a. advancing/retreating b. orienting 4) Effort (Effort Phrasing): a. Effort intensity high/medium/low b. Indication use of each of the Efforts Weight/Space/Time/Flow B. Questionnaire development through pilot studies The first pilot was involved development of the first version of the questionnaire, starting from the point of departure from the previous subsection A. Part of the 2527

questions were focused on measuring the pauses in order to determine states of hesitancy, but the greater part of the coding sheet contained questions that required descriptive answers. An example question, aimed to describe ‘movement signature’ of the participant, was formulated as follows: ‘Body attitude and movement signature: Describe how the participant moves during the experiment and what seems to be characteristic for his/her movement.’ Descriptive questions such as this were in line with the intention of the research, but the answers proved to be difficult to compare and analyze. To be able to determine observer’s agreement, we reformulated the questions in a closed during form for the following pilot. The second pilot aimed at simplifying the comparative analysis. Differently from the first pilot, we created a single sheet on which the answers for all three videos would be collected. By collecting the answers on one sheet, the observers were enabled to compare the kind of answers they provided for each question at the other videos. An example of the need for this is the use of Effort (movement qualities). The observers were asked to give a rating for each movement quality along a sliding scale. The coding sheet was pre-tested by the developers, and they concluded that the answers needed to become more measurable by using the counting of for instance body actions as a deliberate measuring tool. This second version of the questionnaire was tested with 6 observers who were not certified as LMA’s. During this pilot test we found the categories for which observers will strongly disagree. The strong disagreement between the observers is probably related to the difference in training in the detailed and subtle observation of movement compared to the certified observers. This helped the questionnaire developers to re-assess the formulation of the questions in order to make them clearer for the target group of non-experts. In the third pilot instead of giving the observers the freedom to give open-ended answers to questions that related to body actions, the answers were geared towards specification by a) asking the participants to count different body actions, and making possible to see how active the participant responded to the different light behaviors and b) getting a more objective impression of the movement qualities by replacing the sliding scales the option to state whether specific qualities were observed. The third pilot showed that the numeric specification worked well to gain a more guided perception of different behaviors. Therefore, for the final forth pilot the coding sheet was not changed much in terms of what we asked the observers to focus on, but rather on refining once again how we formulated the questions so the observers could be most clear about the task ahead. The forth pilot aimed to test the created questionnaire and is featured in Section IV.

C. Parameters for movement analysis The three pilots discussed in subsection B resulted in the following parameters for the final version of the coding sheet: 1) Category Body a. Body actions. This analysis consists of 2 parts. Part 1 relates to behaviors performed by the participants, without trying to infer the intent or the result of this behavior. For instance, how often does the participant walk from one place to another? / remain standing? (min. 2 seconds) / squat? / bend forwards? Part 2 relates to actions themselves, i.e. is a conscious activity (or series of activities) which has a subjective meaning or goal for the environment, objects, agents, people. For instance, the items of clothing that are touched only / removed from the closet / permanently removed from the closet / looked at & unfolded / inserted back into the closet / moved from one place to another. The observers are told that all body actions should be counted. b. Body orientation. This subcategory is scored in relation to the intelligent agent - the closet. The observers need to score whether the participant is faced towards the closet using: mostly frontal / mostly diagonal or sideways / equal relation between frontal orientation and diagonal or sideways orientation. c. Pauses. A pause is defined as a moment during which the participant is standing still without bodyoriented actions in relation to the closet. The observer needs to score the amount of pauses / pauses that last longer than 2 seconds / pauses that last shorter than 2 seconds. 2) Category Space a. Spatial positioning. This sub-category determines the direction from participant’s spatial position in relation to the intelligent agent (closet). To account for the relativity of this category, the observers need to choose from: Spatial organization in relation to the closet is mostly/some: on the left side of the closet / in the center of the closet / on the right side of the closet. b. Proximity. This sub-category includes two measurable parameters:  The body center in relation to the closet: Mostly near to the closet / Mostly far from the closet;  Does the proximity vary or does it remain stable: Varies / Stays stable.

3) Effort / Movement qualities This category determines the qualitative aspects of interaction with relation to the intelligent agent. In the case of the closet, the observer needs to score the items of clothing that are being touched:  Easy going / relaxed / n.a.  Firmly / delicately / n.a.  Quickly / slowly / n.a.  Directly aimed / scanning / n.a.

2528

In addition, an indication of overall level of energy used when clothing is being touched can be marked as: high/medium/ or low. 4) Concluding description The concluding description requires the observers to give their general impression of how the participant behaves during the experiment. They have to make note of how the participant enters, if the movements are stable or change and to describe how the actions evolve. The observers need to use descriptive words to explain the participant’s movement behavior, i.e. relaxed, hurried, staccato etc. IV. RESULTS The instructions for the 3 CMA observers were as follows. To prepare, the participants needed to be informed in advance about the procedure of the observation scheme and to read all questions on the coding sheet. The next step was to open the video file and to make sure it can be viewed full screen. The questions on the coding sheet did not have to be answered in the proposed order. For the actual analysis, the observers needed to make sure that each coding sheet was marked with the number of the participant and the number of the video; all three videos of the same participant needed to be analyzed before starting a new analysis with another participant; and all questions are to be answered with 1 answer only. If they cannot answer the question as such, should fill in ‘Z’. The answer sheets were consequently returned as locked files and processed for data analysis. To establish the inter-rater reliability we calculated intraclass correlation coefficient (ICC). The categories related to Body actions, Body orientation and Spatial orientation as well as Proximity A, had a good ICC scores. On the Movement qualities, however, the agreement was low. The tests aimed to check if there were any effects due to the Lighting-conditions of the closet. On the Ordinal / Scale level results data a repeated measure ANOVA (3 conditions, within-subject) was performed. Significance for 2 variables: “Bends Forward” and “Permanently Removed from Closet” was found. A main effect was found for the amount a participant “bends forward” across the 3 lighting conditions (P = 0,0610 , which approaches a significance level of 5%). In post-hoc analysis, we can conclude that participants in the submissive condition tend to “bend forward” more often than in the neutral condition. However, no difference was found when comparing the other conditions (neutral + dominant, or submissive + dominant). Main effect was also found for the amount a participant permanently removed an item from the closet, across the 3 lighting conditions (P = 0,0080). In post-hoc analysis, we can conclude that participants in the dominant lighting

condition made the participants to permanently remove items from the closet more than in the neutral condition. However, no difference was found when comparing the other conditions (neutral + submissive, or submissive + dominant). V. CONCLUSIONS We developed a questionnaire that has a far reaching goal to make LMA as a supporting framework during a design process accessible to non-experts. For the purpose of this experiment we only used CMA experts for both creation of the questionnaire and for the evaluation of the participant’s behaviors. So the questionnaire served as a common evaluation framework for the CMA’s to investigate whether according to the LMA, submissive lighting behavior would elicit dominant movement behavior, and vice versa. For the success of the questionnaire design, getting an interrater agreement on the points of departure and movement parameters is sufficient. By unifying the decision mechanism of the observer through a structured questionnaire, we got good agreement for the categories related to Body actions, Body orientation, Spatial orientation and Proximity. However, with regard to the Movement qualities, the agreement was low, which shows that getting agreement on the expressive qualities remains a challenging task and the questionnaire needs to further be developed to also capture these qualities. We further analyzed the results to see whether this questionnaire can complement the analysis on the submissive/dominant behaviors that could have been induced in the human-closet interaction. With respect to submissiveness the participants bended forward more often when the cupboard was expressing dominant behaviors, and this result was calculated to be significant. Bending forward is a typical submissive behavior in human to human interactions. VI. DISCUSSION The aim of the overall study was to challenge two common practices in design and evaluation of intelligent embodied agents. The first one was investigated in a previous study [4]. We tested if individuals react to the behaviors of an expressive agent in the same way that they would react to other individuals. We based our experiments on the dominance/submissive axis of the interpersonal circumplex, both in terms of evaluating the users’ liking of the system and their feelings of dominance or submissiveness in reaction to it [4]. We found that the reactions to the closet are different than predicted by human-human interaction models of communication. For obtaining these results we used observers that are not trained to analyze expressiveness of movement. For a more objective evaluation of results we started looked into the applied use of Laban/Bartenieff Movement Analysis as an evaluation tool in this study. 2529

Using LMA for assessing whether submissive/dominant lighting behavior would elicit opposite movement behavior by participants had limited success. However, this has been, to our knowledge, the first attempt to use LMA to abstract specific movement parameters in order to facilitate a translation to measurable qualities and the method can be used in wide range of studies of human-robot interaction. In this experiment the questionnaire was tested with 3 CMA's. This is a step towards translating the LMA framework to a research-specific questionnaire that can potentially be used by non-experts on Laban Movement Analysis, since most of the used categories do not use Laban-specific knowledge. In addition, non-experts were used in different design phases of the questionnaire.

[7] E. L. van den Broek and J. H. D. M. Westerink, "Considerations for

The final questionnaire uses specific numeric observation of the use of space, body posture and gesture. These actions could be quite easily observed in the videos. Reflecting on the need for specificity with regards to the movement analysis during this experiment, the camera angles were too long-distance in order to get a clear view of movement qualities. Replacing cameras with sensors located on the body that can measure movement qualities related to exertion of force and time will increase the objectivity of judgement. The relationship to space and place is suitable to analyze with a camera such as the current one, but the movement qualities should be measured by wearable sensors. It is important to note that when using the described in this paper evaluation method, gestures and postures specifically related to the contextspecific interaction need to be identified and used.

[12]

ACKNOWLEDGMENTS We would like to thank CMA’s and the postmaster students from User System Interaction program W. van Breukelen, L. Beursgens, R. Haex, L. Perez Romero who took part in the design of the lighting behaviors and in the observation during the second and forth pilot. Special Thanks of M. Ten Bhomer, K. van der Aalst, P. Ross with whom we developed the interactive platform. VII. REFERENCES [1] K. Ducatel, M. Bogdanowicz, F. Scapolo, J. Leijten, and J.-C. [2] [3]

[4]

[5]

[6]

Burgelman, Scenarios for ambient intelligence in 2010: Office for official publications of the European Communities, 2001. A. Schmidt, “Interactive context-aware systems interacting with ambient intelligence.” in Ambient Intelligence, G. Riva, F. Vatalaro, F. Davide and M. Alcañiz, Eds. IOS Press, 2005. Y. Moon and C. I. Nass, "Adaptive agents and personality change: complementarity versus similarity as forms of adaptation," presented at the Conference Companion on Human Factors in Computing Systems, Vancouver, British Columbia, Canada, 1996. L. Hiah, L. Beursgens, R. Haex, L. Perez Romero, T. Yu-Fang, M. ten Bhomer, et al., "Abstract robots with an attitude: Applying interpersonal relation models to human-robot interaction," in ROMAN, 2013 IEEE, pp. 37-44, 2013. M. M. Bradley and P. J. Lang, "Measuring emotion: the selfassessment manikin and the semantic differential," Journal of behavior therapy and experimental psychiatry, vol. 25, pp. 49-59, 1994. E. Barakova, A. Spink, B. de Ruyter, and L. J. J. Noldus, "Trends in measuring human behavior and interaction," Personal and Ubiquitous Computing, vol. 17, pp. 1-2, 2013.

[8]

[9]

[10] [11]

[13]

[14]

[15]

[16]

[17]

[18] [19] [20] [21] [22] [23]

2530

emotion-aware consumer products," Applied Ergonomics, vol. 40, pp. 1055-1064, 2009. T. Lourens, R. van Berkel, and E. Barakova, "Communicating emotions and mental states to robots in a real time parallel framework using Laban movement analysis," Robotics and Autonomous Systems, vol. 58, pp. 1256-1265, 2010. D. Tetteroo, A. Shirzad, M. S. Pereira, M. Zwinderman, L. Duy, and E. Barakova, "Mimicking Expressiveness of Movements by Autistic Children in Game Play," in 2012 International Confernece on Social Computing (SocialCom), pp. 944-949, 2012. E. Barakova and T. Lourens, "Expressing and interpreting emotional movements in social games with robots," Personal and Ubiquitous Computing, vol. 14, pp. 457-467, 2010. A. A. Samadani, E. Kubica, R. Gorbet, and D. Kulić, “Perception and generation of affective hand movements,” International Journal of Social Robotics, vol. 5, no. 1, pp. 35-51, 2013. A. Foroud and S. M. Pellis, "The development of “roughness” in the play fighting of rats: A Laban Movement Analysis perspective," Developmental Psychobiology, vol. 42, pp. 35-43, 2003. P. Vasey, A. Foroud, N. Duckworth, S. Kovacovsky, Male–female and female–female mounting in Japanese macaques: a comparative study of posture and movement, Archives of Sexual Behavior, 35(2), 116–128, 2006. A. Foroud and I. Q. Whishaw, "The consummatory origins of visually guided reaching in human infants: a dynamic integration of whole-body and upper-limb movements," Behavioural brain research, vol. 231, pp. 343-355, 2012. L. P. Dott, Aesthetic listening:contributions of dance/movement therapy to the psychic understanding of motor stereotypes and distortions in autism and psychosis in childhood and adolescence, The Arts in Psychotherapy 22 (3), 241–247, 1995. A. Foroud, I.Q. Whishaw, Changes in the kinematic structure and nonkinematic features of movements during skilled reaching after stroke: a Laban movement analysis in two case studies, Journal of Neuroscience Methods 158 (1), 137–149, 2006. M. ten Bhömer, K. van der Aalst, E. Barakova, and P. Ross, "Product adaptivity through movement analysis: the case of the intelligent walk-in closet," in Proceedings of Design and Semantics of Form and Movement (DeSForM 2009), Taipei, Taiwan, 2009, pp. 114-121. E. B. Goldstein, Sensation & Perception. Wadsworth Publishing, 2007. S. B. Shneiderman and C. Plaisant, "Designing the user interface 4 th edition," ed: Pearson Addison Wesley, USA, 2005. B. Schwartz, A. Tesser, and E. Powell, "Dominance cues in nonverbal behavior," Social Psychology Quarterly, pp. 114-120, 1982. L. Z. Tiedens and A. R. Fragale, "Power moves: Complementarity in dominant and submissive nonverbal behavior," Journal of personality and social psychology, vol. 84, pp. 558-568, 2003. http://notation-motion.sciencesconf.org/ A. Mileounis, R. H. Cuijpers, and E. I. Barakova, "Creating Robots with Personality: The Effect of Personality on Social Intelligence," in Artificial Computation in Biology and Medicine, ed: Springer, 2015, pp. 119-132.