The present capabilities of speech recognition algorithms will be surveyed. The application of IC technology to the implementation of these algorithms will be explored and potential future directions will be determined.

Speech recognition

People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.

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EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century.

During face-to-face interactions, people naturally integrate nonverbal behaviors such as facial expressions and body postures as part of the conversation to infer the communicative intent or emotional state of their interlocutor. The interpretation of these nonverbal behaviors will often be contextualized by interactional cues such as the previous spoken question, the general discussion topic or the physical environment. A critical step in creating computers able to understand or participate in this type of social face-to-face interactions is to develop a computational platform to synchronously recognize nonverbal behaviors as part of the interactional context. In this platform, information for the acoustic and visual modalities should be carefully synchronized and rapidly processed. At the same time, contextual and interactional cues should be remembered and integrated to better interpret nonverbal (and verbal) behaviors. In this article, we introduce a real-time computational framework, MultiSense, which offers flexible and efficient synchronization approaches for context-based nonverbal behavior analysis. MultiSense is designed to utilize interactional cues from both interlocutors (e.g., from the computer and the human participant) and integrate this contextual information when interpreting nonverbal behaviors. MultiSense can also assimilate behaviors over a full interaction and summarize the observed affective states of the user. We demonstrate the capabilities of the new framework with a concrete use case from the mental health domain where MultiSense is used as part of a decision support tool to assess indicators of psychological distress such as depression and post-traumatic stress disorder (PTSD). In this scenario, MultiSense not only infers psychological distress indicators from nonverbal behaviors but also broadcasts the user state in real-time to a virtual agent (i.e., a digital interviewer) designed to conduct semi-structured interviews with human participants. Our experiments show the added value of our multimodal synchronization approaches and also demonstrate the importance of MultiSense contextual interpretation when inferring distress indicators.

MultiSense—Context-Aware Nonverbal Behavior Analysis Framework: A Psychological Distress Use Case

A data stream is a flow of unbounded data that arrives continuously at high speed. In a dynamic streaming environment, the data changes over the time while stream evolves. The evolving nature of data causes essentially the appearance of new concepts. This novel concept could be abnormal such as fraud, network intrusion, or a sudden fall. It could also be a new normal concept that the system has not seen/trained on before. In this paper we propose, develop, and evaluate a technique for concept evolution in evolving data streams. The novel approach continuously monitors the movement of the streaming data to detect any emerging changes. The technique is capable of detecting the emergence of any novel concepts whether they are normal or abnormal. It also applies a continuous and active learning for assimilating the detected concepts in real time. We evaluate our approach on activity recognition domain as an application of evolving data streams. The study of the novel technique on benchmarked datasets showed its efficiency in detecting new concepts and continuous adaptation with low computational cost.

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AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition

The emergence of assistive robots presents the possibility of restoring vital degrees of independence to the elderly and impaired in activities of daily living (ADL). However, one of the main challenges is the lack of a means for effective and intuitive human–robot interaction (HRI). While humans can express their intentions in different ways (e.g., physical gestures or motions, or speech or language patterns), gaze-based implicit intention communication is still underdeveloped. In this study, a novel nonverbal implicit communication framework based on eye gaze is introduced for HRI. In this framework, a user's eye-gaze movements are proactively tracked and analyzed to infer the user's intention in ADL. Then, the inferred intention can be used to command assistive robots for proper service. The advantage of this framework is that gaze-based communication can be handled by most of the people, as it requires very little effort, and most of the elderly and impaired retain visual capability. This framework is expected to simplify HRI, consequently enhancing the adoption of assistive technologies and improving users’ independence in daily living. The testing results of this framework confirmed that a human's subtle gaze cues on visualized objects could be effectively used for human-intention communication. Results also demonstrated that the gaze-based intention communication is easy to learn and use. In this study, the relationship of visual behaviors with the mental process during human intention expression was studied for the first time to build a fundamental understanding of this process. These findings are expected to guide further design of accurate intention inference algorithms and intuitive HRI.

Implicit Intention Communication in Human–Robot Interaction Through Visual Behavior Studies

This paper proposes a new algorithm, the coherence algorithm, for eye movement tracking. Researchers continue to restore some functions lost by handicapped people using some powered electrical and robotic wheelchairs. This paper presents an application of eye tracking method to such wheelchairs. The coherence algorithm tracks the movement of eye towards left or right. The eye blinking feature is also used by the algorithm to control the starting and stopping of wheelchair.

Eye motion tracking for wheelchair control

Inexisting interfaces such as Digital pen which capture only one kind of gesture and in proposed system interfaces it brings a new interaction process. It can recognize both small scale and large scale gestures. An algorithmic framework is for information about Acceleration and Surface Electromyographical (SEMG) signals for gesture identification. In addition, the system consist of a wearable gesture sensing device and an application program along with the algorithmic framework for a mobile phone, will be designed to realize gesture-based real-time interaction. The device which is worn on the forearm, using that device user will be able to manipulate a mobile phone using predefined gestures or even personalized ones. In this system Accelerometer Sensor measure large scale gesture and SEMG sensors measure the small scale gesture. Both this sensors are used for the gesture recognition.

Review on hand gesture based mobile control application

This paper presents a technique of how Eye, Hand Gestures or voice command can be used to control the movement of wheelchair. For Hand gestures we present a scheme which reduces the size of the database which is used to store different postures of human beings which are used by robot as commands. The picture frame may divide into different scan lines and pixel color value under these scan lines are examined to guess the particular posture of user. The robot may use this as command and act accordingly. For interfacing through eye a new algorithm for tracking the movement of eye towards left or right is also proposed. The eye blinking feature can also be used by the algorithm to control the starting and stopping of wheelchair.

Multimod al interface for disabled persons

Design patterns capture software solutions to specific problems that have evolved over time and reflect many iterations of work. Documenting such patterns promotes proven design and software reuse. There has been a growing amount of work documenting design patterns for security, however, little work specific to VoIP security. In 2005 NIST released a report on recommendations and best practices for securing VoIP, however it lacks the structure, terminology, and ease-of understanding needed for both technical and non-technical audiences that is an inherent feature of design patterns. The project will document three design patterns for VoIP implementations related to specific security problems: (1) Encrypt the voice packet with Encryption Technique (2) Key Generation and (3) Decrypt the voice packet against eavesdropping. IPsec module for VoIP is deployed on the Client/Server environment.

Multiple design patterns for voice over IP security

In Recent years data stream classification has been an extensively studied research problem. Data streams are continuous and rapid flow of data. Data streams include Call center records, network traffic data , sensor data and so many other data. Main problem of data streams is its infinite length, concept drift temporal behavior, concept evolution and feature evolutions. It is impractical to store the historical data for training, Because the data streams which consists the historical data are infinite in length. There is a lot of work done on the existing challenges such as concept drift and infinite length, But less concentrated towards Concept evolution. when the new classes or novel classes are invoking in data streams, this scenario is called concept evolution. As we know the existing challenges are concept drift and infinite length, we address concept evolution detection in this paper. In this paper, enhance approach is used for detection of unseen classes in data stream using adaptive outlier detection, discrete Gini coefficient and multiple unseen classes detection.

S. A. Chhabria

Papers

Eye motion tracking for wheelchair control

Review on hand gesture based mobile control application

Multimod al interface for disabled persons

Multiple design patterns for voice over IP security

A review on concept evolution technique on data stream