This article presents an experimental investigation of kinematics of verb sign production in American Sign Language (ASL) using motion capture data. The results confirm that event structure differences in the meaning of the verbs are reflected in the kinematic formation: for example, in the telic verbs (THROW, HIT), the end-point of the event is marked in the verb sign movement by significantly greater deceleration, as compared to atelic verbs (SWIM, TRAVEL). This end-point marker is highly robust regardless of position of the verb in the sentence (medial vs. final), although other prominent kinematic measures, including sign duration and peak speed of dominant hand motion within the sign, are affected by prosodic processes such as Phrase Final Lengthening. The study provides the first kinematic confirmation that event structure is expressed in movement profiles of ASL verbs, up to now only supported by apparent perceptual distinctions. The findings raise further questions about the psychology of event representation both in human languages and in the human mind.

The past decade has seen great advancements in speech recognition for control of interactive devices, personal assistants, and computer interfaces. However, deaf and hard-of-hearing (HoH) individuals, whose primary mode of communication is sign language, cannot use voice-controlled interfaces. Although there has been significant work in video-based sign language recognition, video is not effective in the dark and has raised privacy concerns in the deaf community when used in the context of human ambient intelligence. RF sensors have been recently proposed as a new modality that can be effective under the circumstances where video is not. This article considers the problem of recognizing a trigger sign (wake word) in the context of daily living, where gross motor activities are interwoven with signing sequences. The proposed approach exploits multiple RF data domain representations (time-frequency, range-Doppler, and range-angle) for sequential classification of mixed motion data streams. The recognition accuracy of signs with varying kinematic properties is compared and used to make recommendations on appropriate trigger sign selection for RF-sensor-based user interfaces. The proposed approach achieves a trigger sign detection rate of 98.9% and a classification accuracy of 92% for 15 ASL words and three gross motor activities.

Purpose: Sign language users recruit physical properties of visual motion to convey linguistic information. Research on American Sign Language (ASL) indicates that signers systematically use kinematic features (e.g., velocity, deceleration) of dominant hand motion for distinguishing specific semantic properties of verb classes in production (Malaia & Wilbur, 2012a) and process these distinctions as part of the phonological structure of these verb classes in comprehension (Malaia, Ranaweera, Wilbur, & Talavage, 2012). These studies are driven by the event visibility hypothesis by Wilbur (2003), who proposed that such use of kinematic features should be universal to sign language (SL) by the grammaticalization of physics and geometry for linguistic purposes. In a prior motion capture study, Malaia and Wilbur (2012a) lent support for the event visibility hypothesis in ASL, but there has not been quantitative data from other SLs to test the generalization to other languages. Method: The authors investigated the kinematic parameters of predicates in Croatian Sign Language ( Hrvatskom Znakovnom Jeziku [HZJ]). Results: Kinematic features of verb signs were affected both by event structure of the predicate (semantics) and phrase position within the sentence (prosody). Conclusion: The data demonstrate that kinematic features of motion in HZJ verb signs are recruited to convey morphological and prosodic information. This is the first crosslinguistic motion capture confirmation that specific kinematic properties of articulator motion are grammaticalized in other SLs to express linguistic features.