Multimodal interaction provides the user with multiple modes of interacting with a system. A multimodal interface provides several distinct tools for input and output of data.
Multimodal human-computer interaction refers to the "interaction with the virtual and physical environment through natural modes of communication", This implies that multimodal interaction enables a more free and natural communication, interfacing users with automated systems in both input and output. Specifically, multimodal systems can offer a flexible, efficient and usable environment allowing users to interact through input modalities, such as speech, handwriting, hand gesture and gaze, and to receive information by the system through output modalities, such as speech synthesis, smart graphics and other modalities, opportunely combined. Then a multimodal system has to recognize the inputs from the different modalities combining them according to temporal and contextual constraints in order to allow their interpretation. This process is known as multimodal fusion, and it is the object of several research works from nineties to now. The fused inputs are interpreted by the system. Naturalness and flexibility can produce more than one interpretation for each different modality (channel) and for their simultaneous use, and they consequently can produce multimodal ambiguity generally due to imprecision, noises or other similar factors. For solving ambiguities, several methods have been proposed. Finally the system returns to the user outputs through the various modal channels (disaggregated) arranged according to a consistent feedback (fission).
The pervasive use of mobile devices, sensors and web technologies can offer adequate computational resources to manage the complexity implied by the multimodal interaction. "Using cloud for involving shared computational resources in managing the complexity of multimodal interaction represents an opportunity. In fact, cloud computing allows delivering shared scalable, configurable computing resources that can be dynamically and automatically provisioned and released".
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Human–computer interaction (HCI) is research in the design and the use of computer technology, which focuses on the interfaces between people (users) and computers. HCI researchers observe the ways humans interact with computers and design technologies that allow humans to interact with computers in novel ways. A device that allows interaction between human being and a computer is known as a "Human-computer Interface (HCI)".
Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. Gestures can originate from any bodily motion or state, but commonly originate from the face or hand. Focuses in the field include emotion recognition from face and hand gesture recognition since they are all expressions. Users can make simple gestures to control or interact with devices without physically touching them.
Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context. To date, the most work has been conducted on automating the recognition of facial expressions from video, spoken expressions from audio, written expressions from text, and physiology as measured by wearables.
This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python sou
The course will cover different aspects of multimodal processing (complementarity vs redundancy; alignment and synchrony; fusion), with an emphasis on the analysis of people, behaviors and interaction
The goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real. The course provides a human perception-ac
Viewers of 360-degree videos are provided with both visual modality to characterize their surrounding views and audio modality to indicate the sound direction. Though both modalities are important for saliency prediction, little work has been done by joint ...
Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...
The presence of conversational agents (or chatbots) in educational contexts has been steadily increasing over the past few years. Recent surveys have shown widespread interest in the use of chatbots in education, both for research and practice. Although th ...