Towards next-generation context-aware affective human-machine interfaces

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Until recently, the ubiquitous way of interacting with digital information was via a keyboard and mouse. As mobile devices gained popularity, however, touch became the primary input modality for human-machine interfaces (HMIs), followed closely by voice. The next decade, however, will witness tremendous growth in data generation (e.g., statistics suggest a 50-fold increase in our digital footprint by 2020). New sensors and technologies will emerge and will be embedded in everyday objects, creating the so-called Internet-of-Things revolution. This data deluge will require drastic innovations in human-machine interaction, particularly as the boundaries between humans and machines, as well as machines and the environment become blurred. To achieve this ambitious goal, the proposed research program aims at developing technologies that will enhance machine intelligence by making them not only aware of their surroundings, but also of their users' affective/cognitive states. Such context-aware affective HMIs will revolutionize several key sectors of the Canadian economy, including healthcare, entertainment, education, and telecommunications.***To achieve this long-term goal, six short-term objectives have been defined, grouped under three main themes. First, we propose to take existing context-aware solutions to the next level by i) enabling distributed environment awareness, ii) shifting the human-machine boundary by making the body as an input modality, and iii) giving the machine additional intelligence via an innovative Quality-of-Context metric. Voice and neurophysiological input modalities, however, are extremely sensitive to noise (e.g., room acoustics) and artefacts (e.g., due to movement), respectively. To overcome this severe usability factor, the second proposed theme aims at developing next-generation context-aware enhancement algorithms for both iv) speech and v) neurophysiological signals. Lastly, we propose to vi) build a fully-functional context-aware prototype that uses voice, gesture, and body as HMI input control signals. Validation experiments within a robot control application will be implemented, thus opening doors for longer-term applications, such as telemedicine robots that are aware of the patient's mental state and could, for example, detect the onset of depression and take necessary measures accordingly. ***The proposed research program will place emphasis on excellent training of highly qualified personnel (HQP). HQP will be exposed to an interdisciplinary melange of signal processing, cognitive engineering, machine learning, and hands-on prototyping, as well as to research facilities that feature state-of-the-art wearable technologies and environment monitoring sensors. In addition, via close collaboration with industry, HQP will be equipped with skills and expertise that are in high demand in today's competitive job market. **