This paper has been withdrawn by the authors due to insufficient or definition error(s) in the ethics approval protocol. Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms and an accurate diagnosis. In this work, we contribute a new database consisting of video clips of typical (normal) and atypical (such as hand flapping, spinning or rocking) behaviors, displayed in natural settings, which have been collected from the YouTube video website. We propose a preliminary non-intrusive approach based on skeleton keypoint identification using pretrained deep neural networks on human body video clips to extract features and perform body movement analysis that differentiates typical and atypical behaviors of children. Experimental results on the newly contributed database show that our platform performs best with decision tree as the classifier when compared to other popular methodologies and offers a baseline against which alternate approaches may developed and tested.
@cite_11: We present SocioGlass - a system built on Google Glass paired with a mobile phone that provides a user with in-situ information about an acquaintance in face-to-face communication. The system can recognize faces from the live feed of visual input. Accordingly, it retrieves relevant information about a person with a matching face in the database. In order to provide interaction assistance, multiple aspects of personal information are categorized based on its relevance to the interaction scenario or context. Thus, the system can be adapted to the social context in interaction assistance. The system can be used to help acquaintances build relationships, or to assist people with memory problems.
@cite_4: Presentations have been an effective means of delivering information to groups for ages. Over the past few decades, technological advancements have revolutionized the way humans deliver presentations. Despite that, the quality of presentations can be varied and affected by a variety of reasons. Conventional presentation evaluation usually requires painstaking manual analysis by experts. Although the expert feedback can definitely assist users in improving their presentation skills, manual evaluation suffers from high cost and is often not accessible to most people. In this work, we propose a novel multi-sensor self-quantification framework for presentations. Utilizing conventional ambient sensors (i.e., static cameras, Kinect sensor) and the emerging wearable egocentric sensors (i.e., Google Glass), we first analyze the efficacy of each type of sensor with various nonverbal assessment rubrics, which is followed by our proposed multi-sensor presentation analytics framework. The proposed framework is evaluated on a new presentation dataset, namely NUS Multi-Sensor Presentation (NUSMSP) dataset, which consists of 51 presentations covering a diverse set of topics. The dataset was recorded with ambient static cameras, Kinect sensor, and Google Glass. In addition to multi-sensor analytics, we have conducted a user study with the speakers to verify the effectiveness of our system generated analytics, which has received positive and promising feedback.
@cite_14: 
@cite_26: Autism spectrum disorder (ASD) is a neurodevelopmental disability with atypical traits in behavioral and physiological responses. These atypical traits in individuals with ASD may be too subtle and subjective to measure visually using tedious methods of scoring. Alternatively, the use of intrusive sensors in the measurement of psychophysical responses in individuals with ASD may likely cause inhibition and bias. This paper proposes a novel experimental protocol for non-intrusive sensing and analysis of facial expression, visual scanning, and eye-hand coordination to investigate behavioral markers for ASD. An institutional review board approved pilot study is conducted to collect the response data from two groups of subjects (ASD and control) while they engage in the tasks of visualization, recognition, and manipulation. For the first time in the ASD literature, the facial action coding system is used to classify spontaneous facial responses. Statistical analyses reveal significantly (p <0.01) higher prevalence of smile expression for the group with ASD with the eye-gaze significantly averted (p<0.05) from viewing the face in the visual stimuli. This uncontrolled manifestation of smile without proper visual engagement suggests impairment in reciprocal social communication, e.g., social smile. The group with ASD also reveals poor correlation in eye-gaze and hand movement data suggesting deficits in motor coordination while performing a dynamic manipulation task. The simultaneous sensing and analysis of multimodal response data may provide useful quantitative insights into ASD to facilitate early detection of symptoms for effective intervention planning.
@cite_28: A holistic or appearance-based eigenfeature regularization methodology based on a three-parameter eigenmodel improves computer recognition of human activity.
@cite_9: In this paper, we present a wearable face recognition (FR) system on Google Glass (GG) to assist users in social interactions. FR is the first step towards face-to-face social interactions. We propose a wearable system on GG, which acts as a social interaction assistant, the application includes face detection, eye localization, face recognition and a user interface for personal information display. To be useful in natural social interaction scenarios, the system should be robust to changes in face pose, scale and lighting conditions. OpenCV face detection is implemented in GG. We exploit both OpenCV and ISG (Integration of Sketch and Graph patterns) eye detectors to locate a pair of eyes on the face, between them the former is stable for frontal view faces and the latter performs better for oblique view faces. We extend the eigenfeature regularization and extraction (ERE) face recognition approach by introducing subclass discriminant analysis (SDA) to perform within-subclass discriminant analysis for face feature extraction. The new approach improves the accuracy of FR over varying face pose, expression and lighting conditions. A simple user interface (UI) is designed to present relevant personal information of the recognized person to assist in the social interaction. A standalone independent system on GG and a Client-Server (CS) system via Bluetooth to connect GG with a smart phone are implemented, for different levels of privacy protection. The performance on database created using GG is evaluated and comparisons with baseline approaches are performed. Numerous experimental studies show that our proposed system on GG can perform better real-time FR as compared to other methods.
@cite_6: 
@cite_5: 
@cite_15: Wearable vision brings about new opportunities for augmenting humans in social interactions. However, along with it comes privacy concerns and possible information overload. We explore users' needs and attitudes toward augmented interaction in face-to-face communications. In particular, we want to find out whether users need additional information when interacting with acquaintances, what information they want to access, and how they use it. Based on observations of user behaviors in interactions assisted by Google Glass, we find that users in general appreciated the usefulness of wearable assistance for social interactions. We highlight a few key issues of how wearable devices affect user experience in social interaction.
@cite_10: This paper proposes a color-based video analytic system for quantifying limb movements in epileptic seizure monitoring. The system utilizes colored pyjamas to facilitate limb segmentation and tracking. Thus, it is unobtrusive and requires no sensor marker attached to patient's body. We employ Gaussian mixture models in background foreground modeling and detect limbs through a coarse-to-fine paradigm with graph-cut-based segmentation. Next, we estimate limb parameters with domain knowledge guidance and extract displacement and oscillation features from movement trajectories for seizure detection analysis. We report studies on sequences captured in an epilepsy monitoring unit. Experimental evaluations show that the proposed system has achieved comparable performance to EEG-based systems in detecting motor seizures.
@cite_25: Wearable devices offer immense opportunities in both consumer and enterprise domains due to the hands-free interaction modality and the ability to provide information in real-time. However, due to hardware limitations, it presents a notable challenge to complex applications that have stringent demands on computational efficiency. Leveraging on the computing power of a connected mobile device, we propose a new multi-threaded asynchronous structure to implement opportunistic multi-tasking. We demonstrate an application of the structure for seamless face recognition for social interactions. The experimental studies show that the proposed structure can achieve better performance than a sequential synchronous one. The proposed method can be extended to similar applications in wearable devices.
@cite_17: 
In recent years there has been significant investigation into the use of computer vision methods to identify and classify a number of behavioral atypicalities, including non-intrusive (at a distance) video based monitoring of epileptic seizures in children @cite_10 @cite_17 @cite_6 , human activity recognition @cite_28 @cite_5 and assisting social interactions at various levels @cite_9 @cite_15 @cite_25 @cite_4 @cite_11 @cite_14 . The advantage of computer vision techniques over certain other methods which may involve placement of measuring devices on the subjects body is the non-invasiveness (without interfering the patients) of the method @cite_26 , a carefully placed camera is less likely to interfere with the session and likewise will reduce stress on the child in question. Such non-intrusiveness (measurement at a distance) would not alter the usual natural behavior of the child, thereby, improves the effectiveness of ASD assessment. These attributes are highly desirable by the clinicians and practitioners in the evaluation of ASD among children.