Uttama Lahiri

Design of a Gaze-Sensitive Virtual Social Interactive System for Children With Autism

Impairments in social communication skills are thought to be core deficits in children with autism spectrum disorder (ASD). In recent years, several assistive technologies, particularly Virtual Reality (VR), have been investigated to promote social interactions in this population. It is well known that children with ASD demonstrate atypical viewing patterns during social interactions and thus monitoring eye-gaze can be valuable to design intervention strategies. While several studies have used eye-tracking technology to monitor eye-gaze for offline analysis, there exists no real-time system that can monitor eye-gaze dynamically and provide individualized feedback. Given the promise of VR-based social interaction and the usefulness of monitoring eye-gaze in real-time, a novel VR-based dynamic eye-tracking system is developed in this work. This system, called Virtual Interactive system with Gaze-sensitive Adaptive Response Technology (VIGART), is capable of delivering individualized feedback based on a childs dynamic gaze patterns during VR-based interaction. Results from a usability study with six adolescents with ASD are presented that examines the acceptability and usefulness of VIGART. The results in terms of improvement in behavioral viewing and changes in relevant eye physiological indexes of participants while interacting with VIGART indicate the potential of this novel technology.

An Approach to the Design of Socially Acceptable Robots for Children with Autism Spectrum Disorders

Investigation into technology-assisted intervention for children with autism spectrum disorders (ASD) has gained momentum in recent years. Research suggests that robots could be a viable means to impart skills to this population since children with ASD tend to be fascinated by robots. However, if robots are to be used to impart social skills, a primary deficit for this population, considerable attention needs to be paid to aspects of social acceptability of such robots. Currently there are no design guidelines as to how to develop socially acceptable robots to be used for intervention for children with ASD. As a first step, this work investigates social design of virtual robots for children with ASD. In this paper we describe the design of a virtual environment system for social interaction (VESSI). The design is evaluated through an innovative experiment plan that combines subjective ratings from a clinical observer with physiological responses indicative of affective states from the participants, both collected when participants engage in social tasks with the social robots in a virtual reality environment. Two social parameters of importance for this population, namely eye gaze and social distance, are systematically varied to analyze the response of the participants. The results are presented to illustrate how experiments with virtual social robots can contribute towards the development of future social robots for children with ASD.

Design of a Virtual Reality Based Adaptive Response Technology for Children With Autism

Children with autism spectrum disorder (ASD) demonstrate potent impairments in social communication skills including atypical viewing patterns during social interactions. Recently, several assistive technologies, particularly virtual reality (VR), have been investigated to address specific social deficits in this population. Some studies have coupled eye-gaze monitoring mechanisms to design intervention strategies. However, presently available systems are designed to primarily chain learning via aspects of ones performance only which affords restricted range of individualization. The presented work seeks to bridge this gap by developing a novel VR-based interactive system with Gaze-sensitive adaptive response technology that can seamlessly integrate VR-based tasks with eye-tracking techniques to intelligently facilitate engagement in tasks relevant to advancing social communication skills. Specifically, such a system is capable of objectively identifying and quantifying ones engagement level by measuring real-time viewing patterns, subtle changes in eye physiological responses, as well as performance metrics in order to adaptively respond in an individualized manner to foster improved social communication skills among the participants. The developed system was tested through a usability study with eight adolescents with ASD. The results indicate the potential of the system to promote improved social task performance along with socially-appropriate mechanisms during VR-based social conversation tasks.

Modular neural network-based directional relay for transmission line protection

This letter proposes the application of a modular neural network as a mechanism to discriminate the direction of faults for transmission line protection. The modular neural network approach solves a relatively complex problem by decomposing it into simpler subtasks that are easier to manage and then assembles the solution from the results of the subtasks. In addition to the obvious advantages of a neural network as a fault classifier, incorporation of modularity to the network structure provides more important positive attributes like model complexity reduction, better learning capability, etc. The modular neural network concept has been utilized successfully to develop a directional relay algorithm for a transmission system and subsequently implemented on a DSP TMS320F243 EVM-board.

An Affect-Sensitive Social Interaction Paradigm Utilizing Virtual Reality Environments for Autism Intervention

This paper describes the design and development of both software to create social interaction modules on a virtual reality (VR) platform and individualized affective models for affect recognition of children with autism spectrum disorders (ASD), which includes developing tasks for affect elicitation and using machine-learning mathematical tools for reliable affect recognition. A VR system will be formulated that can present realistic social communication tasks to the children with ASD and can monitor their affective response using physiological signals, such as cardiovascular activities including electrocardiogram, impedance cardiogram, photoplethysmogram, and phonocardiogram; electrodermal activities including tonic and phasic responses from galvanic skin response; electromyogram activities from corrugator supercilii, zygomaticus major, and upper trapezius muscles; and peripheral temperature. This affect-sensitive system will be capable of systematically manipulating aspects of social communication to more fully understand its salient components for children with ASD.

A Physiologically Informed Virtual Reality Based Social Communication System for Individuals with Autism

Clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorders (ASD). This project evaluated the application of a novel physiologically responsive virtual reality based technological system for conversation skills in a group of adolescents with ASD. The system altered components of conversation based on (1) performance alone or (2) the composite effect of performance and physiological metrics of predicted engagement (e.g., gaze pattern, pupil dilation, blink rate). Participants showed improved performance and looking pattern within the physiologically sensitive system as compared to the performance based system. This suggests that physiologically informed technologies may have the potential of being an effective tool in the hands of interventionists.

Development of a novel robot-mediated adaptive response system for joint attention task for children with autism

With Centers for Disease Control and Prevention prevalence estimates for children with autism spectrum disorder (ASD) at 9.1 per 1,000 (1 in 110), identification and effective treatment of ASD is often characterized as a public health emergency. Emerging technology, especially robotic technology, has been shown to be appealing to these children and such interest can be harnessed to address the limitations while providing intervention services to young children with ASD. Generally the spectrum nature of autism calls for intensive, individualized intervention. However, existing robot-mediated systems tend to have limited adaptive capability that limits individualization. Our current work seeks to bridge this gap by developing a novel adaptive and individualized robot-mediated technology for children with ASD. The system is composed of a humanoid robot with its vision being augmented by several wall-mounted cameras for real-time head tracking using a distributed architecture. Based on the cues from the childs head movement, the robot intelligently adapts itself in an individualized manner to promote joint attention. The developed system is validated with two typically developing children. The validation results of the head tracker and the closed-loop nature of interaction are presented.

Classification of gait phases from lower limb EMG: Application to exoskeleton orthosis

This paper describes the use of Bayesian Information Criteria (BIC) along with some standard feature extraction methods and Linear Discriminant Analysis (LDA) classification algorithm to separate 8 different phases of gait by using electromyographic (EMG) signal data of the lower limb. Four time domain features along with 4th order Auto-Regressive model were used to get feature vector set from the EMG data of each leg of an able bodied person. Window of 50 ms (millisecond) was used such that it is within the controller delay limit. Then, the BIC segmentation algorithm was applied on the feature vector sets of 10 different gait cycles one by one to find out the locations of the boundaries between the phases. Due to the differences in the identified boundary locations for different gait cycles, the ambiguous part around each boundary was removed. The LDA classifier was then applied to the EMG feature vector set to classify 8 phases of gait. The classification accuracy increased by a significant amount in comparison to when BIC algorithm was not used. The work is our first step towards making an EMG signal driven foot-knee exoskeleton orthosis for the stroke patient having hemiparesis.

Understanding the Psycho-Physiological Implications of Interaction With a Virtual Reality-Based System in Adolescents With Autism: A Feasibility Study

Individuals with Autism are characterized by deficits in socialization and communication. In recent years several assistive technologies, e.g., Virtual Reality (VR), have been investigated to address the socialization deficits in these individuals. Presently available VR-based systems address various aspects of social communication in an isolated manner and without monitoring ones affective state such as, anxiety. However, in conventional observation-based therapy, a therapist adjusts the intervention paradigm by monitoring ones anxiety level. But, often these individuals have an inherent inability to explicitly express their anxiety thereby inducing limitations on conventional techniques. Physiological signals being continuously available and not directly impacted by these communication difficulties can be alternatively used as markers of ones anxiety level. In our research we aim at designing a Virtual-reality bAsed Social-communication Task (VAST) system that can address the various aspects of social communication, e.g., social context, subtle social cues, emotional expression, etc., in a cumulative and structured way. In addition, we augment this with a capability to use ones physiological signals as markers of ones anxiety level. In our preliminary feasibility study we investigate the potential of VAST to cause variations in ones performance and anxiety level that can be mapped from ones physiological indices.

Design of a virtual reality based adaptive response technology for children with autism spectrum disorder

Impairments in social communication skills are thought to be core deficits in children with autism spectrum disorder (ASD). In recent years, several assistive technologies, particularly Virtual Reality (VR), have been investigated to promote social interactions in this population. It is well-known that these children demonstrate atypical viewing patterns during social interactions and thus monitoring eye-gaze can be valuable to design intervention strategies. However, presently available VR-based systems are designed to chain learning via aspects of ones performance only permitting limited degree of individualization. Given the promise of VR-based social interaction and the usefulness of monitoring eye-gaze in real-time, a novel VR-based dynamic eye-tracking system is developed in this work. The developed system was tested through a small usability study with four adolescents with ASD. The results indicate the potential of the system to promote improved social task performance along with socially-appropriate mechanisms during VR-based social conversation tasks.