AMAB: Automated measurement and analysis of body motion

Abstract

Technologies that measure human nonverbal behavior have existed for some time, and their use in the analysis of social behavior has become more popular following the development of sensor technologies that record full-body movement. However, a standardized methodology to efficiently represent and analyze full-body motion is absent. In this article, we present automated measurement and analysis of body motion (AMAB), a methodology for examining individual and interpersonal nonverbal behavior from the output of full-body motion tracking systems. We address the recording, screening, and normalization of the data, providing methods for standardizing the data across recording condition and across subject body sizes. We then propose a series of dependent measures to operationalize common research questions in psychological research. We present practical examples from several application areas to demonstrate the efficacy of our proposed method for full-body measurements and comparisons across time, space, body parts, and subjects. Keywords Motion capture . Human motion analysis . Measurement of body motion . Body motion analysisNonverbal behavior is a key ingredient in personal expression (McNeill, 1985) and the regulation of interpersonal exchanges (Ekman, 1965). Its analysis has contributed significantly to our understanding of how human interaction works. It is perhaps not surprising, then, that researchers continue to develop methods for the effective measurement and analysis of such behavior. The most common approach relies on observational coding of behavior, using classification schemes that are developed to serve a particular research question (Grammer, Kruck, & Magnusson, 1998;Lausberg & Sloetjes, 2009). These schemes are often evaluative in nature, in the sense that researchers code for the occurrence of particular forms of communication, such as gestures (Doron, Beattie, & Shovelton, 2010) or facial expressions (Vick et al., 2006). Others are "physicalistic" coding procedures that utilize a more precise mapping of behavior by quantifying the movement of different limbs (Bente, 1989;Dael, Mortillaro, & Scherer, 2012;Frey & Von Cranach, 1973). While the evaluative schemes are open to issues of reliability because of the qualitative component of the coding (Scherer & Ekman, 1982), the latter physicalistic schemes have been shown to yield reliable annotations that are sufficiently detailed to animate computer characters (Bente, Petersen, Krämer, & De Ruiter, 2001). However, for both approaches, the derivation of the data through coding is time consuming, meaning that there is often an inherent trade-off between the number of coded actions and the amount of coded material.In an effort to circumvent this difficulty, there has been a growing trend toward using technologies to evaluate behavior (Altorfer et al., 2000;Bente, Senokozlieva, Pennig, Al-Issa, & Fischer, 2008). In particular, researchers have started to undertake automatic measurement of human movement with motion capture devices. To date, such a...

Kinect-taped communication

“…There has been an emerging need in behavioral science research to automate the measurement of nonverbal behaviors [24]. Our current use of Kinect is exploratory, and our automatic measures will need to be further validated with new and replicated studies.…”

Similarity-Based Processing of Motion Capture Data

“…To effectively model the spatial and temporal evolutions of different motions, robust and sufficiently discriminative features need to be extracted [30]. To become invariant towards the subject's position, orientation and skeleton size, the input data are often normalized [22]. The normalized data are then processed to extract features on the level of frames or segments.…”

Review on 3D Facial Animation Techniques

“…As this approach evolving through years, the recorded data of motion capture are in form of series of either 3D trajectories (Wang et al, 2012;Chen & Koskela, 2013) or rotation angles (Sedmidubsky et al, 2013) of body joints. Motion data that obtained are usually being normalized (Poppe et al, 2014) to become invariant towards few aspects that curb fair comparison like variety of human skeleton size or different facing directions. Numerous combinations of modalities can be done (Chen & Koskela, 2013) which eventually helps on focusing simultaneously of multiple motion.…”