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Great ape gestures: intentional communication with a rich set of innate signals
Great apes give gestures deliberately and voluntarily, in order to influence particular target audiences, whose direction of attention they take into account when choosing which type of gesture to use. These facts make the study of ape gesture directly relevant to understanding the evolutionary precursors of human language; here we present an assessment of ape gesture from that perspective, focusing on the work of the “St Andrews Group” of researchers. Intended meanings of ape gestures are relatively few and simple. As with human words, ape gestures often have several distinct meanings, which are effectively disambiguated by behavioural context. Compared to the signalling of most other animals, great ape gestural repertoires are large. Because of this, and the relatively small number of intended meanings they achieve, ape gestures are redundant, with extensive overlaps in meaning. The great majority of gestures are innate, in the sense that the species’ biological inheritance includes the potential to develop each gestural form and use it for a specific range of purposes. Moreover, the phylogenetic origin of many gestures is relatively old, since gestures are extensively shared between different genera in the great ape family. Acquisition of an adult repertoire is a process of first exploring the innate species potential for many gestures and then gradual restriction to a final (active) repertoire that is much smaller. No evidence of syntactic structure has yet been detected.
Journal Article
A systematic review on hand gesture recognition techniques, challenges and applications
With the development of today's technology, and as humans tend to naturally use hand gestures in their communication process to clarify their intentions, hand gesture recognition is considered to be an important part of Human Computer Interaction (HCI), which gives computers the ability of capturing and interpreting hand gestures, and executing commands afterwards. The aim of this study is to perform a systematic literature review for identifying the most prominent techniques, applications and challenges in hand gesture recognition.
To conduct this systematic review, we have screened 560 papers retrieved from IEEE Explore published from the year 2016 to 2018, in the searching process keywords such as \"hand gesture recognition\" and \"hand gesture techniques\" have been used. However, to focus the scope of the study 465 papers have been excluded. Only the most relevant hand gesture recognition works to the research questions, and the well-organized papers have been studied.
The results of this paper can be summarized as the following; the surface electromyography (sEMG) sensors with wearable hand gesture devices were the most acquisition tool used in the work studied, also Artificial Neural Network (ANN) was the most applied classifier, the most popular application was using hand gestures for sign language, the dominant environmental surrounding factor that affected the accuracy was the background color, and finally the problem of overfitting in the datasets was highly experienced.
The paper will discuss the gesture acquisition methods, the feature extraction process, the classification of hand gestures, the applications that were recently proposed, the challenges that face researchers in the hand gesture recognition process, and the future of hand gesture recognition. We shall also introduce the most recent research from the year 2016 to the year 2018 in the field of hand gesture recognition for the first time.
Journal Article
Ten lectures on spoken language and gesture from the perspective of cognitive linguistics : issues of dynamicity and multimodality
\"Cognitive linguistics is purported to be a usage-based approach, yet only recently has research in some of its subfields turned to spontaneous spoken (versus written) language data. The collection of Alan Cienki's 'Ten Lectures on Spoken Language and Gesture from the Perspective of Cognitive Linguistics' considers what it means to apply different approaches from within this field to the dynamic, multimodal combination of speech and gesture. The lectures encompass such main paradigms as blending and mental space theory, conceptual metaphor and metonymy, construction and cognitive grammars, image schemas, and mental simulation in relation to semantics. Overall, Alan Cienki shows that taking the usage-based commitment seriously with audio-visual data raises new issues and questions for theoretical models in cognitive linguistics.\"--Cover page 4.
Book
Italian Sign Language from a Cognitive and Socio-Semiotic Perspective
This volume reveals new insights on the faculty of language. By proposing a new approach in the analysis and description of Italian Sign Language (LIS), that can be extended also to other sign languages.
eBook
Signs and gestures : non-verbal communication in the Qatari culture
Body language; nonverbal communication; communication and culture; Qatar.
BOOK
Gestural repair in Mandarin conversation
Ever since Charles Goodwin’s seminal works on gaze, there has been a long-standing interest in Conversation Analysis in the interrelationship between talk and bodily conduct in the accomplishment of social action. Recently, a small but emerging body of research has explored the ways in which embodied conduct figures in the organization and operations of repair. In this article, I take up a similar theme and investigate the interaction between talk and iconic gestures in same-turn self-initiated repair in Mandarin conversation. The phenomenon I examine concerns the use of what I call “gestural repair.” The analysis focuses on how such repair can intertwine with talk in multi-stage operations in the progressivity and resolution of repair. The data are drawn from 50 hours of naturally-occurring conversations collected in China. Some unique features of such gestural repair observed in the Mandarin data are also discussed.
Journal Article
Embodied interaction : language and body in the material world
\"How do people organize their body movement and talk when they interact with one another in the material world? How do they coordinate linguistic structures with bodily resources (such as gaze and gesture) to bring about coherent and intelligible courses of action? How are physical settings, artifacts, technologies, and non-linguistic sign-systems implicated in social interaction and shared cognition? This volume brings together advanced work by leading international scholars who share video-based research methods that integrate semiotic, linguistic, sociological, anthropological, and cognitive science perspectives with detailed, microanalytic observations. Collectively they provide a coherent framework for analyzing the production of meaning and the organization of social interaction in the complex and heterogeneous settings that are characteristic of modern life: ranging from ordinary and bilingual conversation to family interaction, and from daycare centers to work settings such as airplanes, clinics, and architects' offices, and to activities such as auctions and musical performances. Several chapters investigate how participants with communicative impairments (aphasia, blindness, deafness) creatively build meaning with others. Embodied Interaction is indispensable for anyone interested in the study of language and social interaction. This volume will be a point of reference for future research on multimodality in human communication and action\"--Provided by publisher.
Book
Gesture-Based Physical Stability Classification and Rehabilitation System
This paper introduces the Gesture-Based Physical Stability Classification and Rehabilitation System (GPSCRS), a low-cost, non-invasive solution for evaluating physical stability using an Arduino microcontroller and the DFRobot Gesture and Touch sensor. The system quantifies movement smoothness, consistency, and speed by analyzing “up” and “down” hand gestures over a fixed period, generating a Physical Stability Index (PSI) as a single metric to represent an individual’s stability. The system focuses on a temporal analysis of gesture patterns while incorporating placeholders for speed scores to demonstrate its potential for a comprehensive stability assessment. The performance of various machine learning and deep learning models for gesture-based classification is evaluated, with neural network architectures such as Transformer, CNN, and KAN achieving perfect scores in recall, accuracy, precision, and F1-score. Traditional machine learning models such as XGBoost show strong results, offering a balance between computational efficiency and accuracy. The choice of model depends on specific application requirements, including real-time constraints and available resources. The preliminary experimental results indicate that the proposed GPSCRS can effectively detect changes in stability under real-time conditions, highlighting its potential for use in remote health monitoring, fall prevention, and rehabilitation scenarios. By providing a quantitative measure of stability, the system enables early risk identification and supports tailored interventions for improved mobility and quality of life.
Journal Article