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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...

We present a new signal for detecting deception: full body motion. Previous work on detecting deception from body movement has relied either on human judges or on specific gestures (such as fidgeting or gaze aversion) that are coded by humans. While this research has helped to build the foundation of the field, results are often characterized by inconsistent and contradictory findings, with small-stakes lies under lab conditions detected at rates little better than guessing. We examine whether a full body motion capture suit, which records the position, velocity, and orientation of 23 points in the subject’s body, could yield a better signal of deception. Interviewees of South Asian ( n = 60) or White British culture ( n = 30) were required to either tell the truth or lie about two experienced tasks while being interviewed by somebody from their own ( n = 60) or different culture ( n = 30). We discovered that full body motion–the sum of joint displacements–was indicative of lying 74.4% of the time. Further analyses indicated that including individual limb data in our full body motion measurements can increase its discriminatory power to 82.2%. Furthermore, movement was guilt- and penitential-related, and occurred independently of anxiety, cognitive load, and cultural background. It appears that full body motion can be an objective nonverbal indicator of deceit, showing that lying does not cause people to freeze.

PurposeCybercrime rates have increased rapidly during the last couple of decades, resulting in cybercrimes becoming common crimes. However, most victims do not report cybercrimes to the police. Therefore, this study examines reporting cybercrime victimization and provides insights into the role of the police in this process.Design/methodology/approachA sample of 595 individuals was used. All respondents were shown three vignettes about hypothetical cybercrime victimization and were asked to imagine that this situation happened to them. Four crime and reporting characteristics were manipulated across vignettes. Respondents' intentions to report to the police and to other organizations were used as the dependent variables in regression analyses. Four random factors in the vignettes (i.e. type of crime, seriousness of crime, victim–perpetrator relationship, and reporting modality), as well as several characteristics of the respondents were included in the regression models as independent variables.FindingsThe type of cybercrime is the most important predictor for reporting behaviors. Other determinants are: more serious offenses were more often reported and offenses are less often reported in situations where the victim personally knows the perpetrator. Furthermore, there is large discrepancy between intended and actual cybercrime reporting. These findings provide valuable insights into the factors that influence reporting behavior in the real world. Only a fifth of respondents indicated that they would not report cybercrime victimization to the police. This implies that attempts at improving reporting rates should not solely be focused on improving people's attitudes, but also on removing obstacles to turn these attitudes into actions.Originality/valueIn the current study, the authors contribute to the existing literature by asking a large sample from the general population in the Netherlands about both their intended reporting behavior (i.e. a vignette study) and their actual reporting behavior (i.e. self-reports) of victimization of a wide variety of different types of cybercrime. Determinants of both reporting to the police as well as to other organizations are examined. Moreover, respondents are asked about motivations behind their decision to (not) report a cybercrime to the police. Last, people were asked about their past experiences with reporting cybercrime victimization to the police.