Analysis and enhancement of interpersonal coordination using inertial measurement unit solutions

Die heutigen mobilen Kommunikationstechnologien haben den Umfang der verbalen und textbasierten Kommunikation mit anderen Menschen, sozialen Robotern und künstlicher Intelligenz erhöht. Auf der anderen Seite reduzieren diese Technologien die nonverbale und die direkte persönliche Kommunikation, was zu einer gesellschaftlichen Thematik geworden ist, weil die Verringerung der direkten persönlichen Interaktionen eine angemessene Wahrnehmung sozialer und umgebungsbedingter Reizmuster erschweren und die Entwicklung allgemeiner sozialer Fähigkeiten bremsen könnte. Wissenschaftler haben aktuell die Bedeutung nonverbaler zwischenmenschlicher Aktivitäten als soziale Fähigkeiten untersucht, indem sie menschliche Verhaltensmuster in Zusammenhang mit den jeweilgen neurophysiologischen Aktivierungsmustern analzsiert haben. Solche Querschnittsansätze werden auch im Forschungsprojekt der Europäischen Union "Socializing sensori-motor contingencies" (socSMCs) verfolgt, das darauf abzielt, die Leistungsfähigkeit sozialer Roboter zu verbessern und Autismus-Spektrumsstörungen (ASD) adäquat zu behandeln. In diesem Zusammenhang ist die Modellierung und das Benchmarking des Sozialverhaltens gesunder Menschen eine Grundlage für theorieorientierte und experimentelle Studien zum weiterführenden Verständnis und zur Unterstützung interpersoneller Koordination. In diesem Zusammenhang wurden zwei verschiedene empirische Kategorien in Abhängigkeit von der Entfernung der Interagierenden zueinander vorgeschlagen: distale vs. proximale Interaktionssettings, da sich die Struktur der beteiligten kognitiven Systeme zwischen den Kategorien ändert und sich die Ebene der erwachsenden socSMCs verschiebt. Da diese Dissertation im Rahmen des socSMCs-Projekts entstanden ist, wurden Interaktionssettings für beide Kategorien (distal und proximal) entwickelt. Zudem wurden Ein-Sensor-Lösungen zur Reduzierung des Messaufwands (und auch der Kosten) entwickelt, um eine Messung ausgesuchter Verhaltensparameter bei einer Vielzahl von Menschen und sozialen Interaktionen zu ermöglichen. Zunächst wurden Algorithmen für eine kopfgetragene Trägheitsmesseinheit (H-IMU) zur Messung der menschlichen Kinematik als eine Ein-Sensor-Lösung entwickelt. Die Ergebnisse bestätigten, dass die H-IMU die eigenen Gangparameter unabhängig voneinander allein auf Basis der Kopfkinematik messen kann. Zweitens wurden—als ein distales socSMC-Setting—die interpersonellen Kopplungen mit einem Bezug auf drei interagierende Merkmale von „Übereinstimmung“ (engl.: rapport) behandelt: Positivität, gegenseitige Aufmerksamkeit und Koordination. Die H-IMUs überwachten bestimmte soziale Verhaltensereignisse, die sich auf die Kinematik der Kopforientierung und Oszillation während des Gehens und Sprechens stützen, so dass der Grad der Übereinstimmung geschätzt werden konnte. Schließlich belegten die Ergebnisse einer experimentellen Studie, die zu einer kollaborativen Aufgabe mit der entwickelten IMU-basierten Tablet-Anwendung durchgeführt wurde, unterschiedliche Wirkungen verschiedener audio-motorischer Feedbackformen für eine Unterstützung der interpersonellen Koordination in der Kategorie proximaler sensomotorischer Kontingenzen. Diese Dissertation hat einen intensiven interdisziplinären Charakter: Technologische Anforderungen in den Bereichen der Sensortechnologie und der Softwareentwicklung mussten in direktem Bezug auf vordefinierte verhaltenswissenschaftliche Fragestellungen entwickelt und angewendet bzw. gelöst werden—und dies in zwei unterschiedlichen Domänen (distal, proximal). Der gegebene Bezugsrahmen wurde als eine große Herausforderung bei der Entwicklung der beschriebenen Methoden und Settings wahrgenommen. Die vorgeschlagenen IMU-basierten Lösungen könnten dank der weit verbreiteten IMU-basierten mobilen Geräte zukünftig in verschiedene Anwendungen perspektiv reich integriert werden.Today’s mobile communication technologies have increased verbal and text-based communication with other humans, social robots and intelligent virtual assistants. On the other hand, the technologies reduce face-to-face communication. This social issue is critical because decreasing direct interactions may cause difficulty in reading social and environmental cues, thereby impeding the development of overall social skills. Recently, scientists have studied the importance of nonverbal interpersonal activities to social skills, by measuring human behavioral and neurophysiological patterns. These interdisciplinary approaches are in line with the European Union research project, “Socializing sensorimotor contingencies” (socSMCs), which aims to improve the capability of social robots and properly deal with autism spectrum disorder (ASD). Therefore, modelling and benchmarking healthy humans’ social behavior are fundamental to establish a foundation for research on emergence and enhancement of interpersonal coordination. In this research project, two different experimental settings were categorized depending on interactants’ distance: distal and proximal settings, where the structure of engaged cognitive systems changes, and the level of socSMCs differs. As a part of the project, this dissertation work referred to this spatial framework. Additionally, single-sensor solutions were developed to reduce costs and efforts in measuring human behaviors, recognizing the social behaviors, and enhancing interpersonal coordination. First of all, algorithms using a head worn inertial measurement unit (H-IMU) were developed to measure human kinematics, as a baseline for social behaviors. The results confirmed that the H-IMU can measure individual gait parameters by analyzing only head kinematics. Secondly, as a distal sensorimotor contingency, interpersonal relationship was considered with respect to a dynamic structure of three interacting components: positivity, mutual attentiveness, and coordination. The H-IMUs monitored the social behavioral events relying on kinematics of the head orientation and oscillation during walk and talk, which can contribute to estimate the level of rapport. Finally, in a new collaborative task with the proposed IMU-based tablet application, results verified effects of different auditory-motor feedbacks on the enhancement of interpersonal coordination in a proximal setting. This dissertation has an intensive interdisciplinary character: Technological development, in the areas of sensor and software engineering, was required to apply to or solve issues in direct relation to predefined behavioral scientific questions in two different settings (distal and proximal). The given frame served as a reference in the development of the methods and settings in this dissertation. The proposed IMU-based solutions are also promising for various future applications due to widespread wearable devices with IMUs.European Commission/HORIZON2020-FETPROACT-2014/641321/E

Directional adposition use in English, Swedish and Finnish

Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellä (in front of) and jäljessä (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003). When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellä (in front of) and jäljessä (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected. We asked native English, Swedish and Finnish speakers’ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers. All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion. We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion. Levinson, S. C. (1996). Frames of reference and Molyneux’s question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press. Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press. Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo

Artificial Companions with Personality and Social Role

Subtitle: "Expectations from Users on the Design of Groups of Companions"International audienceRobots and virtual characters are becoming increasingly used in our everyday life. Yet, they are still far from being able to maintain long-term social relationships with users. It also remains unclear what future users will expect from these so-called "artificial companions" in terms of social roles and personality. These questions are of importance because users will be surrounded with multiple artificial companions. These issues of social roles and personality among a group of companions are sledom tackled in user studies. In this paper, we describe a study in which 94 participants reported that social roles and personalities they would expect from groups of companions. We explain how the resulsts give insights for the design of future groups of companions endowed with social intelligence

Embodied interaction with visualization and spatial navigation in time-sensitive scenarios

Paraphrasing the theory of embodied cognition, all aspects of our cognition are determined primarily by the contextual information and the means of physical interaction with data and information. In hybrid human-machine systems involving complex decision making, continuously maintaining a high level of attention while employing a deep understanding concerning the task performed as well as its context are essential. Utilizing embodied interaction to interact with machines has the potential to promote thinking and learning according to the theory of embodied cognition proposed by Lakoff. Additionally, the hybrid human-machine system utilizing natural and intuitive communication channels (e.g., gestures, speech, and body stances) should afford an array of cognitive benefits outstripping the more static forms of interaction (e.g., computer keyboard). This research proposes such a computational framework based on a Bayesian approach; this framework infers operator\u27s focus of attention based on the physical expressions of the operators. Specifically, this work aims to assess the effect of embodied interaction on attention during the solution of complex, time-sensitive, spatial navigational problems. Toward the goal of assessing the level of operator\u27s attention, we present a method linking the operator\u27s interaction utility, inference, and reasoning. The level of attention was inferred through networks coined Bayesian Attentional Networks (BANs). BANs are structures describing cause-effect relationships between operator\u27s attention, physical actions and decision-making. The proposed framework also generated a representative BAN, called the Consensus (Majority) Model (CMM); the CMM consists of an iteratively derived and agreed graph among candidate BANs obtained by experts and by the automatic learning process. Finally, the best combinations of interaction modalities and feedback were determined by the use of particular utility functions. This methodology was applied to a spatial navigational scenario; wherein, the operators interacted with dynamic images through a series of decision making processes. Real-world experiments were conducted to assess the framework\u27s ability to infer the operator\u27s levels of attention. Users were instructed to complete a series of spatial-navigational tasks using an assigned pairing of an interaction modality out of five categories (vision-based gesture, glove-based gesture, speech, feet, or body balance) and a feedback modality out of two (visual-based or auditory-based). Experimental results have confirmed that physical expressions are a determining factor in the quality of the solutions in a spatial navigational problem. Moreover, it was found that the combination of foot gestures with visual feedback resulted in the best task performance (p\u3c .001). Results have also shown that embodied interaction-based multimodal interface decreased execution errors that occurred in the cyber-physical scenarios (p \u3c .001). Therefore we conclude that appropriate use of interaction and feedback modalities allows the operators maintain their focus of attention, reduce errors, and enhance task performance in solving the decision making problems

Automatic Context-Driven Inference of Engagement in HMI: A Survey

An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection. Therefore, to develop successful human-centered human-machine interaction applications, automatic engagement inference is one of the tasks required to achieve engaging interactions between humans and machines, and to make machines attuned to their users, hence enhancing user satisfaction and technology acceptance. Several factors contribute to engagement state inference, which include the interaction context and interactants' behaviours and identity. Indeed, engagement is a multi-faceted and multi-modal construct that requires high accuracy in the analysis and interpretation of contextual, verbal and non-verbal cues. Thus, the development of an automated and intelligent system that accomplishes this task has been proven to be challenging so far. This paper presents a comprehensive survey on previous work in engagement inference for human-machine interaction, entailing interdisciplinary definition, engagement components and factors, publicly available datasets, ground truth assessment, and most commonly used features and methods, serving as a guide for the development of future human-machine interaction interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement perception modules are integrated sets apart the presented survey from existing surveys

Gesture and Speech in Interaction - 4th edition (GESPIN 4)

International audienceThe fourth edition of Gesture and Speech in Interaction (GESPIN) was held in Nantes, France. With more than 40 papers, these proceedings show just what a flourishing field of enquiry gesture studies continues to be. The keynote speeches of the conference addressed three different aspects of multimodal interaction:gesture and grammar, gesture acquisition, and gesture and social interaction. In a talk entitled Qualitiesof event construal in speech and gesture: Aspect and tense, Alan Cienki presented an ongoing researchproject on narratives in French, German and Russian, a project that focuses especially on the verbal andgestural expression of grammatical tense and aspect in narratives in the three languages. Jean-MarcColletta's talk, entitled Gesture and Language Development: towards a unified theoretical framework,described the joint acquisition and development of speech and early conventional and representationalgestures. In Grammar, deixis, and multimodality between code-manifestation and code-integration or whyKendon's Continuum should be transformed into a gestural circle, Ellen Fricke proposed a revisitedgrammar of noun phrases that integrates gestures as part of the semiotic and typological codes of individuallanguages. From a pragmatic and cognitive perspective, Judith Holler explored the use ofgaze and hand gestures as means of organizing turns at talk as well as establishing common ground in apresentation entitled On the pragmatics of multi-modal face-to-face communication: Gesture, speech andgaze in the coordination of mental states and social interaction.Among the talks and posters presented at the conference, the vast majority of topics related, quitenaturally, to gesture and speech in interaction - understood both in terms of mapping of units in differentsemiotic modes and of the use of gesture and speech in social interaction. Several presentations explored the effects of impairments(such as diseases or the natural ageing process) on gesture and speech. The communicative relevance ofgesture and speech and audience-design in natural interactions, as well as in more controlled settings liketelevision debates and reports, was another topic addressed during the conference. Some participantsalso presented research on first and second language learning, while others discussed the relationshipbetween gesture and intonation. While most participants presented research on gesture and speech froman observer's perspective, be it in semiotics or pragmatics, some nevertheless focused on another importantaspect: the cognitive processes involved in language production and perception. Last but not least,participants also presented talks and posters on the computational analysis of gestures, whether involvingexternal devices (e.g. mocap, kinect) or concerning the use of specially-designed computer software forthe post-treatment of gestural data. Importantly, new links were made between semiotics and mocap data

Towards Modelling Trust in Voice at Zero Acquaintance

Trust is essential in many human relationships, especially where there is an element of inter-dependency. However, humans tend to make quick judgements about trusting other individuals, even those met at zero acquaintance. Past studies have shown the significance of voice in perceived trustworthiness, but research associating trustworthiness and different vocal features such as speech rate and fundamental frequency (f0) has yet to yield consistent results. Therefore, this paper proposes a method to investigate 1) the association between trustworthiness and different vocal features, 2) the vocal characteristics that Malaysian ethnic groups base their judgement of trustworthiness on and 3) building a neural network model that predicts the degree of trustworthiness in a human voice. In the method proposed, a reliable set of audio clips will be obtained and analyzed with SoundGen to determine the acoustical characteristics. Then the audio clips will be distributed to a large group of untrained respondents to rate their degree of trust in the speakers of each audio clip. The participants will be able to choose from 30 sets of audio clips which will consist of 6 audio clips each. The acoustic characteristics will be analyzed and com-pared with the ratings to determine if there are any correlations between the acoustic characteristic and the trustworthiness ratings. After that, a neural network model will be built based on the collected data. The neural network model will be able to predict the trustworthiness of a person’s voice. Keywords—prosody, trust, voice, vocal cues, zero acquaintance