A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment

We describe the design and implementation of a vision based interactive entertainment system that makes use of both involuntary and voluntary control paradigms. Unintentional input to the system from a potential viewer is used to drive attention-getting output and encourage the transition to voluntary interactive behaviour. The iMime system consists of a character animation engine based on the interaction metaphor of a mime performer that simulates non-verbal communication strategies, without spoken dialogue, to capture and hold the attention of a viewer. The system was developed in the context of a project studying care of dementia sufferers. Care for a dementia sufferer can place unreasonable demands on the time and attentional resources of their caregivers or family members. Our study contributes to the eventual development of a system aimed at providing relief to dementia caregivers, while at the same time serving as a source of pleasant interactive entertainment for viewers. The work reported here is also aimed at a more general study of the design of interactive entertainment systems involving a mixture of voluntary and involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho

Do That, There: An Interaction Technique for Addressing In-Air Gesture Systems

When users want to interact with an in-air gesture system, they must first address it. This involves finding where to gesture so that their actions can be sensed, and how to direct their input towards that system so that they do not also affect others or cause unwanted effects. This is an important problem [6] which lacks a practical solution. We present an interaction technique which uses multimodal feedback to help users address in-air gesture systems. The feedback tells them how (“do that”) and where (“there”) to gesture, using light, audio and tactile displays. By doing that there, users can direct their input to the system they wish to interact with, in a place where their gestures can be sensed. We discuss the design of our technique and three experiments investigating its use, finding that users can “do that” well (93.2%–99.9%) while accurately (51mm–80mm) and quickly (3.7s) finding “there”

Face and Body gesture recognition for a vision-based multimodal analyser

users, computers should be able to recognize emotions, by analyzing the human's affective state, physiology and behavior. In this paper, we present a survey of research conducted on face and body gesture and recognition. In order to make human-computer interfaces truly natural, we need to develop technology that tracks human movement, body behavior and facial expression, and interprets these movements in an affective way. Accordingly in this paper, we present a framework for a vision-based multimodal analyzer that combines face and body gesture and further discuss relevant issues

Do That, There: An Interaction Technique for Addressing In-Air Gesture Systems

When users want to interact with an in-air gesture system, they must first address it. This involves finding where to gesture so that their actions can be sensed, and how to direct their input towards that system so that they do not also affect others or cause unwanted effects. This is an important problem [6] which lacks a practical solution. We present an interaction technique which uses multimodal feedback to help users address in-air gesture systems. The feedback tells them how (“do that”) and where (“there”) to gesture, using light, audio and tactile displays. By doing that there, users can direct their input to the system they wish to interact with, in a place where their gestures can be sensed. We discuss the design of our technique and three experiments investigating its use, finding that users can “do that” well (93.2%–99.9%) while accurately (51mm–80mm) and quickly (3.7s) finding “there”

An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices

In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe