Automatically Detected Nonverbal Behavior Predicts Creativity in Collaborating Dyads

Won, Andrea Stevenson; Bailenson, Jeremy N.; Stathatos, Suzanne C.; Dai, Wenqing

doi:10.1007/s10919-014-0186-0

Cited by 57 publications

(53 citation statements)

References 29 publications

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“…In contrast, pairs who had not experienced a prior interaction (i.e., control condition) only realised productivity benefits to the extent that in-phase synchrony emerged spontaneously (cf. Won et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Coordination Matters: Interpersonal Synchrony Influences Collaborative Problem-Solving

Miles¹,

Lumsden²,

Flannigan³

et al. 2017

PSYCH

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Moving in time with others is a central characteristic of social life and has been shown to promote a host of social-cognitive attunements (e.g., person memory, affiliation, prosociality) for those involved. Less attention has been paid, however, to how the effects of coordination can serve higher-order goal-directed social behaviour. Here we explored whether interpersonal synchrony impacts performance on a collaborative problem-solving task. One hundred and ninety two participants completed a short movement exercise in pairs whereby coordination mode was manipulated (in-phase synchrony, asynchrony, control). Each pair then jointly discussed a problem-solving exercise while the degree to which coordination spontaneously emerged was assessed. The results revealed that collaboration was more effective following in-phase coordination. Of theoretical significance, both instructed and spontaneous synchrony were associated with better performance, with the short-term history of each dyad shaping precisely when coordination was functional. Overall, the synchronization of body movements appears to support effective collaboration.

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Section: Discussionmentioning

confidence: 99%

Coordination Matters: Interpersonal Synchrony Influences Collaborative Problem-Solving

Miles¹,

Lumsden²,

Flannigan³

et al. 2017

PSYCH

View full text Add to dashboard Cite

show abstract

“…It took up to 30 hours per team to generate the interaction data, which is far too long for any immediate practical application. However, the controlled setting of the interaction task makes an automated instrumentation more accessible through sensor-based technology as employed in studies by Curhan and Pentland [2007] who used a wearable sensor badge or by Won et al [2014] who used two Kinect movement sensors to capture interaction dynamics. Especially studies that rely on sensors that need to be installed in the environment such as the Kinect sensors can benefit from the use of an elicitation protocol as it lets researchers select the exact time and place of observation.…”

Section: Thin-slicing Effortmentioning

confidence: 99%

Coupling Interactions and Performance: Predicting Team Performance from Thin Slices of Conflict

Jung

2016

ACM Trans. Comput.-Hum. Interact.

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Do teams show stable conflict interaction patterns that predict their performance hours, weeks, or even months in advance? Two studies demonstrate that two of the same patterns of emotional interaction dynamics that distinguish functional from dysfunctional marriages also distinguish high from low-performance design teams in the field, up to 6 months in advance, with up to 91% accuracy, and based on just 15minutes of interaction data: Group Affective Balance, the balance of positive to negative affect during an interaction, and Hostile Affect, the expression of a set of specific negative behaviors were both found as predictors of team performance. The research also contributes a novel method to obtain a representative sample of a team's conflict interaction. Implications for our understanding of design work in teams and for the design of groupware and feedback intervention systems are discussed.

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“…They have been applied not only for gaming but to various important branches of technique and science. Among those we can mention natural user interfaces and robotics [27], education [28], medicine [29], zootechnics [30], intelligent home technology [31,32] nonverbal behavior analysis [33]. As information and communication technology continues to evolve, body sensory technologies provide learning designers new approaches to facilitating learning in an innovative way [34].…”

Section: Application Of Depth Sensors In Actions Recognitionmentioning

confidence: 99%

Application of Assistive Computer Vision Methods to Oyama Karate Techniques Recognition

2015

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Abstract:In this paper we propose a novel algorithm that enables online actions segmentation and classification. The algorithm enables segmentation from an incoming motion capture (MoCap) data stream, sport (or karate) movement sequences that are later processed by classification algorithm. The segmentation is based on Gesture Description Language classifier that is trained with an unsupervised learning algorithm. The classification is performed by continuous density forward-only hidden Markov models (HMM) classifier. Our methodology was evaluated on a unique dataset consisting of MoCap recordings of six Oyama karate martial artists including multiple champion of Kumite Knockdown Oyama karate. The dataset consists of 10 classes of actions and included dynamic actions of stands, kicks and blocking techniques. Total number of samples was 1236. We have examined several HMM classifiers with various number of hidden states and also Gaussian mixture model (GMM) classifier to empirically find the best setup of the proposed method in our dataset. We have used leave-one-out cross validation. The recognition rate of our methodology differs between karate techniques and is in the range of 81% ± 15% even to 100%. Our method is not limited for this class of actions but can be easily adapted to any other MoCap-based actions. The description of our approach and its evaluation are the main contributions of this paper. The results presented in this paper are effects of pioneering research on online karate action classification.

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Automatically Detected Nonverbal Behavior Predicts Creativity in Collaborating Dyads

Cited by 57 publications

References 29 publications

Coordination Matters: Interpersonal Synchrony Influences Collaborative Problem-Solving

Coordination Matters: Interpersonal Synchrony Influences Collaborative Problem-Solving

Coupling Interactions and Performance: Predicting Team Performance from Thin Slices of Conflict

Application of Assistive Computer Vision Methods to Oyama Karate Techniques Recognition

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