BodySpace: inferring body pose for natural control of a music player

We describe the BodySpace system, which uses inertial sensing and pattern recognition to allow the gestural control of a music player by placing the device at different parts of the body. We demonstrate a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based techniques can shape gestural interaction

Pre-Interaction Identification by Dynamic Grip Classification

We present a novel authentication method to identify users as they pick up a mobile device. We use a combination of back-of-device capacitive sensing and accelerometer measurements to perform classification, and obtain increased performance compared to previous accelerometer-only approaches. Our initial results suggest that users can be reliably identified during the pick-up movement before interaction commences

Model-based target sonification on mobile devices

We investigate the use of audio and haptic feedback to augment the display of a mobile device controlled by tilt input. We provide an example of this based on Doppler effects, which highlight the user's approach to a target, or a target's movement from the current state, in the same way we hear the pitch of a siren change as it passes us. Twelve participants practiced navigation/browsing a state-space that was displayed via audio and vibrotactile modalities. We implemented the experiment on a Pocket PC, with an accelerometer attached to the serial port and a headset attached to audio port. Users navigated through the environment by tilting the device. Feedback was provided via audio displayed via a headset, and by vibrotactile information displayed by a vibrotactile unit in the Pocket PC. Users selected targets placed randomly in the state-space, supported by combinations of audio, visual and vibrotactile cues. The speed of target acquisition and error rate were measured, and summary statistics on the acquisition trajectories were calculated. These data were used to compare different display combinations and configurations. The results in the paper quantified the changes brought by predictive or 'quickened' sonified displays in mobile, gestural interaction

Multimodal, Embodied and Location-Aware Interaction

This work demonstrates the development of mobile, location-aware, eyes-free applications which utilise multiple sensors to provide a continuous, rich and embodied interaction. We bring together ideas from the fields of gesture recognition, continuous multimodal interaction, probability theory and audio interfaces to design and develop location-aware applications and embodied interaction in both a small-scale, egocentric body-based case and a large-scale, exocentric `world-based' case. BodySpace is a gesture-based application, which utilises multiple sensors and pattern recognition enabling the human body to be used as the interface for an application. As an example, we describe the development of a gesture controlled music player, which functions by placing the device at different parts of the body. We describe a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based interaction techniques and the use of real world constraints can shape the gestural interaction. GpsTunes is a mobile, multimodal navigation system equipped with inertial control that enables users to actively explore and navigate through an area in an augmented physical space, incorporating and displaying uncertainty resulting from inaccurate sensing and unknown user intention. The system propagates uncertainty appropriately via Monte Carlo sampling and output is displayed both visually and in audio, with audio rendered via granular synthesis. We demonstrate the use of uncertain prediction in the real world and show that appropriate display of the full distribution of potential future user positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data. We show that this system enables eyes-free navigation around set trajectories or paths unfamiliar to the user for varying trajectory width and context. We demon- strate the possibility to create a simulated model of user behaviour, which may be used to gain an insight into the user behaviour observed in our field trials. The extension of this application to provide a general mechanism for highly interactive context aware applications via density exploration is also presented. AirMessages is an example application enabling users to take an embodied approach to scanning a local area to find messages left in their virtual environment

Multimodal, Embodied and Location-Aware Interaction

This work demonstrates the development of mobile, location-aware, eyes-free applications which utilise multiple sensors to provide a continuous, rich and embodied interaction. We bring together ideas from the fields of gesture recognition, continuous multimodal interaction, probability theory and audio interfaces to design and develop location-aware applications and embodied interaction in both a small-scale, egocentric body-based case and a large-scale, exocentric `world-based' case. BodySpace is a gesture-based application, which utilises multiple sensors and pattern recognition enabling the human body to be used as the interface for an application. As an example, we describe the development of a gesture controlled music player, which functions by placing the device at different parts of the body. We describe a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based interaction techniques and the use of real world constraints can shape the gestural interaction. GpsTunes is a mobile, multimodal navigation system equipped with inertial control that enables users to actively explore and navigate through an area in an augmented physical space, incorporating and displaying uncertainty resulting from inaccurate sensing and unknown user intention. The system propagates uncertainty appropriately via Monte Carlo sampling and output is displayed both visually and in audio, with audio rendered via granular synthesis. We demonstrate the use of uncertain prediction in the real world and show that appropriate display of the full distribution of potential future user positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data. We show that this system enables eyes-free navigation around set trajectories or paths unfamiliar to the user for varying trajectory width and context. We demon- strate the possibility to create a simulated model of user behaviour, which may be used to gain an insight into the user behaviour observed in our field trials. The extension of this application to provide a general mechanism for highly interactive context aware applications via density exploration is also presented. AirMessages is an example application enabling users to take an embodied approach to scanning a local area to find messages left in their virtual environment

PainDroid: An android-based virtual reality application for pain assessment

Earlier studies in the field of pain research suggest that little efficient intervention currently exists in response to the exponential increase in the prevalence of pain. In this paper, we present an Android application (PainDroid) with multimodal functionality that could be enhanced with Virtual Reality (VR) technology, which has been designed for the purpose of improving the assessment of this notoriously difficult medical concern. Pain- Droid has been evaluated for its usability and acceptability with a pilot group of potential users and clinicians, with initial results suggesting that it can be an effective and usable tool for improving the assessment of pain. Participant experiences indicated that the application was easy to use and the potential of the application was similarly appreciated by the clinicians involved in the evaluation. Our findings may be of considerable interest to healthcare providers, policy makers, and other parties that might be actively involved in the area of pain and VR research