Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%)

Latent-Dynamic Discriminative Models for Continuous Gesture Recognition

Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper we develop a discriminative framework for simultaneous sequence segmentation and labeling which can capture both intrinsic and extrinsic class dynamics. Our approach incorporates hidden state variables which model the sub-structure of a class sequence and learn the dynamics between class labels. Each class label has a disjoint set of associated hidden states, which enables efficient training and inference in our model. We evaluated our method on the task of recognizing human gestures from unsegmented video streams and performed experiments on three different datasets of head and eye gestures. Our results demonstrate that our model for visual gesture recognition outperform models based on Support Vector Machines, Hidden Markov Models, and Conditional Random Fields

HGaze Typing: head-gesture assisted gaze typing

This paper introduces a bi-modal typing interface, HGaze Typing, which combines the simplicity of head gestures with the speed of gaze inputs to provide efficient and comfortable dwell-free text entry. HGaze Typing uses gaze path information to compute candidate words and allows explicit activation of common text entry commands, such as selection, deletion, and revision, by using head gestures (nodding, shaking, and tilting). By adding a head-based input channel, HGaze Typing reduces the size of the screen regions for cancel/deletion buttons and the word candidate list, which are required by most eye-typing interfaces. A user study finds HGaze Typing outperforms a dwell-time-based keyboard in efficacy and user satisfaction. The results demonstrate that the proposed method of integrating gaze and head-movement inputs can serve as an effective interface for text entry and is robust to unintended selections.https://dl.acm.org/doi/pdf/10.1145/3448017.3457379Published versio

A graphical model based solution to the facial feature point tracking problem

In this paper a facial feature point tracker that is motivated by applications such as human-computer interfaces and facial expression analysis systems is proposed. The proposed tracker is based on a graphical model framework. The facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated on real video data under various conditions including occluded facial gestures and head movements. It is also compared to two popular methods, one based on Kalman filtering exploiting temporal relations, and the other based on active appearance models (AAM). Improvements provided by the proposed approach are demonstrated through both visual displays and quantitative analysis

Facial Feature Tracking and Occlusion Recovery in American Sign Language

Facial features play an important role in expressing grammatical information in signed languages, including American Sign Language(ASL). Gestures such as raising or furrowing the eyebrows are key indicators of constructions such as yes-no questions. Periodic head movements (nods and shakes) are also an essential part of the expression of syntactic information, such as negation (associated with a side-to-side headshake). Therefore, identification of these facial gestures is essential to sign language recognition. One problem with detection of such grammatical indicators is occlusion recovery. If the signer's hand blocks his/her eyebrows during production of a sign, it becomes difficult to track the eyebrows. We have developed a system to detect such grammatical markers in ASL that recovers promptly from occlusion. Our system detects and tracks evolving templates of facial features, which are based on an anthropometric face model, and interprets the geometric relationships of these templates to identify grammatical markers. It was tested on a variety of ASL sentences signed by various Deaf native signers and detected facial gestures used to express grammatical information, such as raised and furrowed eyebrows as well as headshakes.National Science Foundation (IIS-0329009, IIS-0093367, IIS-9912573, EIA-0202067, EIA-9809340

Coordinated Eye and Head Movements for Gaze Interaction in 3D Environments

Gaze is attractive for interaction, as we naturally look at objects we are interested in. As a result, gaze has received significant attention within human-computer interaction as an input modality. However, gaze has been limited to only eye movements in situations where head movements are not expected to be used or as head movements in an approximation of gaze when an eye tracker is unavailable. From these observations arise an opportunity and a challenge: we propose to consider gaze as multi-modal in line with psychology and neuroscience research to more accurately represent user movements. The natural coordination of eye and head movements could then enable the development of novel interaction techniques to further the possibilities of gaze as an input modality. However, knowledge of the eye and head coordination in 3D environments and its usage for interaction design is limited. This thesis explores eye and head coordination and their potential for interaction in 3D environments by developing interaction techniques that aim to tackle established gaze-interaction issues. We study fundamental eye, head, and body movements in virtual reality during gaze shifts. From the study results, we design interaction techniques and applications that avoid the Midas touch issue, allow expressive gaze- based interaction, and handle eye tracking accuracy issues. We ground the evaluation of our interaction techniques through empirical studies. From the techniques and study results, we define three design principles for coordinated eye and head interaction from these works that distinguish between eye- only and head-supported gaze shifts, eye-head alignment as input, and distinguishing head movements for gestures and head movements that naturally occur to support gaze. We showcase new directions for gaze-based interaction and present a new way to think about gaze by taking a more comprehensive approach to gaze interaction and showing that there is more to gaze than just the eyes

The role of non-verbal communication in second language learner and native speaker discourse

It is undeniable that non-verbal signals exert a profound impact on communication. Many researchers proved that people, when they are hesitating, analyze non-verbal signals to comprehend the meaning of a message (Allen, 1999), because they prioritize non-verbal aspects of communication over the verbal ones. The role of non-verbal communication is much more profound when native/non-native discourse is taken into consideration (Allen, 1999; Gregersen, 2007). The aim of the present paper is to analyze non-verbal communication of a native speaker and a second language learner. The main emphasis is put especially on the differences between the non-verbal signals of second language learners and native speakers. Some of these differences may disturb or prevent the interlocutors from conveying a message in learner/native speaker discourse (Marsh et al., 2003) so it is necessary to raise awareness of cultural differences and underline the tremendous role of non-verbal communication in second language learning. Furthermore, the present paper also covers some suggestions for foreign language teachers in order to improve their knowledge of the body language of their learners in the target language and help them to raise awareness of the significance of non-verbal communication in second language discourse

Surface electromyographic control of a novel phonemic interface for speech synthesis

Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of eight training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.F31 DC014872 - NIDCD NIH HHS; R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; T90 DA032484 - NIDA NIH HHShttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesishttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesisPublished versio