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Just as vocalization proceeds in a continuous stream in speech, so too do movements of the hands, face, and body in sign languages. Here, we use motion-capture technology to distinguish lexical signs in sign language from other common types of expression in the signing stream. One type of expression is constructed action, the enactment of (aspects of) referents and events by (parts of) the body. Another is classifier constructions, the manual representation of analogue and gradient motions and locations simultaneously with specified referent morphemes. The term signing is commonly used for all of these, but we show that not all visual signals in sign languages are of the same type. In this study of Israeli Sign Language, we use motion capture to show that the motion of lexical signs differs significantly along several kinematic parameters from that of the two other modes of expression: constructed action and the classifier forms. In so doing, we show how motion-capture technology can help to define the universal linguistic category "word," and to distinguish it from the expressive gestural elements that are commonly found across sign languages. [ABSTRACT FROM AUTHOR]

The authors introduce a novel marker-less multi-camera setup that allows easy synchronisation between 3D cameras as well as a novel pose estimation method that is calculated on the fly based on the human body being tracked, and thus requires no calibration session nor special calibration equipment. They show high accuracy in both calibration and data merging and is on par with equipment-based calibration. They deduce several insights and practical guidelines for the camera setup and for the preferred data merging methods. Finally, they present a test case that computerises the Fugl-Meyer stroke rehabilitation protocol using the authors' multi-sensor capture system. They conducted a Helsinki-approved research in a hospital in which they collected data on stroke patients and healthy subjects using their multi-camera system. Spatio-temporal features were extracted from the acquired data and machine learning-based evaluations were applied. Results showed that patients and healthy subjects can be correctly classified at a rate of above 90%. Furthermore, they show that the most significant features in the classification are strongly correlated with the Fugl-Meyer guidelines. This demonstrates the feasibility of a low-cost, flexible and non-invasive motion capture system that can potentially be operated in a home setting. [ABSTRACT FROM AUTHOR]

Phonetic reduction arises in the course of typical language production, when language users produce a less clearly articulated form of a word. An important factor that affects phonetic reduction is the predictability of the information conveyed: predictable information is reduced. This can be observed in everyday use of reference in spoken language. Specifically, first mention of a referent is more carefully articulated than subsequent mentions of the same referents, which are often phonetically reduced. Here we ask whether phonetic reduction for predictable information exists in a young sign language, and, in particular, how phonetic reduction is realized in visual languages that exploit various articulators of the body: the hands, the head, and the torso. The only natural languages that we can observe as they emerge in real time are young sign languages, and we focus on one of these in the current study: Israeli Sign Language (ISL). We use 3D motion-capture technology to measure phonetic reduction in signers of ISL by comparing the production of referring expressions synchronically, at different points during a narrative (e.g., Introduction, Reintroduction, Maintenance). Our findings show: (a) that phonetic reduction is present in a young sign language; and specifically (b) that the actions of different articulators involved in discourse are reduced, based on predictability. We consider the importance of these findings in understanding predictability in language more generally. [ABSTRACT FROM AUTHOR]