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Computer and internet based questionnaires have become a standard tool in Human-Computer Interaction research and other related fields, such as psychology and sociology. Amazon’s Mechanical Turk (AMT) service is a new method of recruiting participants and conducting certain types of experiments. This study compares whether participants recruited through AMT give different responses than participants recruited through an online forum or recruited directly on a university campus. Moreover, we compare whether a study conducted within AMT results in different responses compared to a study for which participants are recruited through AMT but which is conducted using an external online questionnaire service. The results of this study show that there is a statistical difference between results obtained from participants recruited through AMT compared to the results from the participant recruited on campus or through online forums. We do, however, argue that this difference is so small that it has no practical consequence. There was no significant difference between running the study within AMT compared to running it with an online questionnaire service. There was no significant difference between results obtained directly from within AMT compared to results obtained in the campus and online forum condition. This may suggest that AMT is a viable and economical option for recruiting participants and for conducting studies as setting up and running a study with AMT generally requires less effort and time compared to other frequently used methods. We discuss our findings as well as limitations of using AMT for empirical studies.

Augmented Reality (AR), the overlay of virtual images onto the real world, is an increasingly popular technique for developing new human-computer interfaces. As human navigation and orientation in different environments depend on both visual and auditory information, sound plays a very important role in AR applications. In this paper we explore users' capability to localize a spatial sound (registered with a virtual object) in an AR environment, under different spatial configurations of the virtual scene. The results not only confirm several previous findings on sound localization, but also point out some important new visual-audio cues which should be taken into consideration for effective localization and orientation in AR environment. Finally, this paper provides tentative guidelines for adding spatial sound to AR environments.

This paper discusses an evaluation of an augmented reality (AR) multimodal interface that uses combined speech and paddle gestures for interaction with virtual objects in the real world. We briefly describe our AR multimodal interface architecture and multimodal fusion strategies that are based on the combination of time-based and domain semantics. Then, we present the results from a user study comparing using multimodal input to using gesture input alone. The results show that a combination of speech and paddle gestures improves the efficiency of user interaction. Finally, we describe some design recommendations for developing other multimodal AR interfaces.

For individuals with high degrees of motor disability or locked-in syndrome, it is impractical or impossible to use mechanical switches to interact with electronic devices. Brain computer interfaces (BCIs) can use motor imagery to detect interaction intention from users but lack the accuracy of mechanical switches. Hence, there exists a strong need to improve the accuracy of EEG-based motor imagery BCIs attempting to implement an on/off switch. Here, we investigate how monitoring the pupil diameter of a person as a psycho-physiological parameter in addition to traditional EEG channels can improve the classification accuracy of a switch-like BCI. We have recently noticed in our lab (work not yet published) how motor imagery is associated with increases in pupil diameter when compared to a control rest condition. The pupil diameter parameter is easily accessible through video oculography since most gaze tracking systems report pupil diameter invariant to head position. We performed a user study with 30 participants using a typical EEG based motor imagery BCI. We used common spatial patterns to separate motor imagery, signaling movement intention, from a rest control condition. By monitoring the pupil diameter of the user and using this parameter as an additional feature, we show that the performance of the classifier trying to discriminate motor imagery from a control condition improves over the traditional approach using just EEG derived features. Given the limitations of EEG to construct highly robust and reliable BCIs, we postulate that multi-modal approaches, such as the one presented here that monitor several psycho-physiological parameters, can be a successful strategy in making BCIs more accurate and less vulnerable to constraints such as requirements for long training sessions or high signal to noise ratio of electrode channels.

This paper describes an augmented reality (AR) multimodal interface that uses speech and paddle gestures for interaction. The application allows users to intuitively arrange virtual furniture in a virtual room using a combination of speech and gestures from a real paddle. Unlike other multimodal AR applications, the multimodal fusion is based on the combination of time-based and semantic techniques to disambiguate a users speech and gesture input. We describe our AR multimodal interface architecture and discuss how the multimodal inputs are semantically integrated into a single interpretation by considering the input time stamps, the object properties, and the user context.

A proof of concept system is developed to provide a broad assessment of speech development issues in children. It has been designed to enable non-experts to complete an initial screening of children's speech with the aim of reducing the workload on Speech Language Pathology services. The system was composed of an acoustic model trained by neural networks with split temporal context features and a constrained HMM encoded with the knowledge of Speech Language Pathologists. Results demonstrated the system was able to improve PER by 33% compared with standard HMM decoders, with a minimum PER of 19.03% achieved. Identification of Phonological Error Patterns with up to 94% accuracy was achieved despite utilizing only a small corpus of disordered speech from Australian children. These results indicate the proposed system is viable and the direction of further development are outlined in the paper.

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