Body Language Recognition Research Articles

FORMING THE REQUIREMENTS FOR SIGN LANGUAGE DETECTION

Sign language is a way of communicating through body movements. Body language recognition has been one of the most challenging research problems in the last few years. Their recognition plays an increasingly important role due to the huge spread of digital technologies. With the development of the fields of deep learning and computer vision, researchers have developed various automatic motion language recognition methods that can understand body movement. The idea of this study is to examine the existing sign language recognition systems in the world. These works are mainly divided into sensor-based systems and vision-based systems. Studies have shown that sensor-based tracking is more resource-intensive and difficult to implement than traditional image-based research, and there are also combinations of these two methods. As a result of this study, it can be seen that there are many sign languages in the world, most of which do not have their own databases available, and also dynamic gesture recognition systems still need new research to improve the results. During the work, several points have been formed that can help improve the quality of the work by adding studies and databases for unknown languages, using them also to obtain an acceptable accuracy of dynamic gesture detection, as well as ensuring the system work in real-time and use of few resources.

The Influence of Immersive Virtual Reality Systems on Online Social Application

This study presents a face-to-face simulation of social interaction based on scene roaming, real-time voice capture, and action capture. This paper aimed to compare the difference between social and traditional plane social communication, analyzing its advantages and shortcomings. In particular, we designed an experiment to study the performance of face-to-face simulation based on virtual reality technology, observing the adaptability of the user to the system and the accuracy of body language recognition. We developed an immersive virtual reality social application (IVRSA), which has a basic social function. As an experimental platform, IVRSA uses Unity3D as its engine, and HTC VIVE as an external input/output device, with voice communication, immersive virtual tour, and head and hand movement simulation functions. We recruited 30 volunteers for the test. The test consisted of two parts. The first part was to provide a news topic for volunteers to freely communicate in IVRSA and WeChat. After communication, we used questionnaires to obtain feedback results of the test and compared the two social applications. In the second part, some volunteers were given a list of actions, which they were asked to describe to the rest of volunteers in the form of body expression, letting them guess the action they were performing. After the end of test, the accuracy rate and the time used were analyzed. Results showed that user’s intention expression efficiency was very high in the immersive virtual reality social system. Body tracking devices with more tracking points can provide better body expression effect.

Deficits of emotional body language recognition in college students with schizotypal traits: an event-related potential study

Deficits of emotional body language recognition in college students with schizotypal traits: an event-related potential study

A socially assistive robot that can interpret affective body language during one-on-one human-robot interactions

Socially assistive robots can engage in assistive human-robot interactions (HRI) by providing rehabilitation of cognitive, social, and physical abilities after a stroke, accident or diagnosis of a social, developmental or cognitive disorder. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: a robot’s ability to identify human non-verbal communication during assistive interactions. Namely, we present a unique non-contact automated sensory-based approach for identification and categorisation of human upper body language in determining how accessible a person is to a robot during natural real-time HRI. This classification will allow a robot to effectively determine its own reactive task-driven behaviour during assistive interactions. The types of interactions envisioned include providing reminders, health monitoring, and social and cognitive therapies. Preliminary experiments presented show the potential of integrating the proposed body language recognition and classification technique into a socially assistive robot partaking participating in HRI scenarios.

Human Body Pose Interpretation and Classification for Social Human-Robot Interaction

A novel breed of robots known as socially assistive robots is emerging. These robots are capable of providing assistance to individuals through social and cognitive interaction. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: The robot’s ability to identify human non-verbal communication during assistive interactions. In particular, we present a unique non-contact and non-restricting automated sensor-based approach for identification and categorization of human upper body language in determining how accessible a person is to the robot during natural real-time human-robot interaction (HRI). This classification will allow a robot to effectively determine its own reactive task-driven behavior during assistive interactions. Human body language is an important aspect of communicative nonverbal behavior. Body pose and position can play a vital role in conveying human intent, moods, attitudes and affect. Preliminary experiments show the potential of integrating the proposed body language recognition and classification technique into socially assistive robotic systems partaking in HRI scenarios.

Instrumental Music Influences Recognition of Emotional Body Language

In everyday life, emotional events are perceived by multiple sensory systems. Research has shown that recognition of emotions in one modality is biased towards the emotion expressed in a simultaneously presented but task irrelevant modality. In the present study, we combine visual and auditory stimuli that convey similar affective meaning but have a low probability of co-occurrence in everyday life. Dynamic face-blurred whole body expressions of a person grasping an object while expressing happiness or sadness are presented in combination with fragments of happy or sad instrumental classical music. Participants were instructed to categorize the emotion expressed by the visual stimulus. The results show that recognition of body language is influenced by the auditory stimuli. These findings indicate that crossmodal influences as previously observed for audiovisual speech can also be obtained from the ignored auditory to the attended visual modality in audiovisual stimuli that consist of whole bodies and music.

Human and animal sounds influence recognition of body language

In naturalistic settings emotional events have multiple correlates and are simultaneously perceived by several sensory systems. Recent studies have shown that recognition of facial expressions is biased towards the emotion expressed by a simultaneously presented emotional expression in the voice even if attention is directed to the face only. So far, no study examined whether this phenomenon also applies to whole body expressions, although there is no obvious reason why this crossmodal influence would be specific for faces. Here we investigated whether perception of emotions expressed in whole body movements is influenced by affective information provided by human and by animal vocalizations. Participants were instructed to attend to the action displayed by the body and to categorize the expressed emotion. The results indicate that recognition of body language is biased towards the emotion expressed by the simultaneously presented auditory information, whether it consist of human or of animal sounds. Our results show that a crossmodal influence from auditory to visual emotional information obtains for whole body video images with the facial expression blanked and includes human as well as animal sounds.

Human-Computer Chinese Sign Language Interaction System

The generation and recognition of body language is a key technologies of VR. Sign Language is a visual-gestural language mainly used by hearing-impaired people. In this paper, gesture and facial expression models are created using computer graphics and used to synthesize Chinese Sign Language (CSL), and from it a human-computer CSL interaction system is implemented. Using a system combining CSL synthesis and CSL recognition subsystem, hearing-impaired people with data-gloves can pantomime CSL, which can then be displayed on the computer screen in real time and translated into Chinese text. Hearing people can also use the system by entering Chinese text, which is translated into CSL and displayed on the computer screen. In this way hearing-impaired people and hearing people can communicate with each other conveniently.

Violence in the Accident and Emergency Department.

Crimes of violence are recorded increasingly frequently, including those involving health professionals. We reviewed records of violent incidents kept for a major Accident and Emergency Department over a ten-year period. Details were recorded in a Violent Incident Book by all grades of A/E staff, and separate records were kept by hospital security officers. A total of 407 incidents were recorded. Numbers, rank and sex of staff assaulted, types of assault, injuries received, weapons used and characteristics and disposal of perpetrators were recorded. Many were young males who had been drinking: others were regular attenders, of whom three subsequently died and one convicted of murder. Nurses and male doctors appeared to be at the greatest risk of assault and receptionists at the least risk. Recording of violent incidents and subsequent prosecution seemed inconsistent, and may have reflected the lack of a code of practice in this area. Suggestions are made about preventing, predicting and dealing with violence, and its aftermath, in the A and E department, including the use of security officers and closed circuit television, waiting room design, the recognition of body language and signs of alcohol or substance intoxication. The importance of staff support after an assault is emphasized, including immediate and long-term counselling, provision of legal advice, criminal or civil court action, victim support schemes and the workings of the Criminal Injuries Compensation Board. Free legal advice for staff assaulted at work should be included in the terms of service of NHS staff.