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

迈向下一代情境感知情感人机界面

• 批准号: RGPIN-2016-04175
• 负责人: Falk, Tiago
• 金额: $ 3.28万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689149
• 关键词: Towards; next; generation; context; aware

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. **

直到最近，与数字信息交互的无处不在的方式是通过键盘和鼠标进行的。然而，随着移动设备的流行，触摸成为人机接口（HMIS）的主要输入方式，然后是声音。然而，接下来的十年将见证数据生成的巨大增长（例如，统计数据表明到2020年，我们的数字足迹增加了50倍）。新的传感器和技术将出现，并将嵌入到日常物体中，从而创造所谓的The-Things Internet Revolution。这些数据洪水将需要在人机相互作用中进行巨大的创新，尤其是当人与机器之间的边界以及机器和环境变得模糊时。为了实现这一雄心勃勃的目标，拟议的研究计划旨在开发技术，这些技术将通过使它们不仅了解周围环境，而且还要了解用户的情感/认知状态来增强机器智能。这种感知的情感HMI将彻底改变加拿大经济的几个关键部门，包括医疗保健，娱乐，教育和电信。首先，我们建议通过i）实现分布式环境意识，将现有的上下文感知解决方案提高到一个新的水平，ii）通过使身体作为输入模式来移动人机边界，以及iiii）通过创新的封闭质量质质量测量，为机器提供了额外的智能。但是，语音和神经生理输入方式分别对噪声（例如房间声学）和人工制品（例如，由于运动）非常敏感。为了克服这个严重的可用性因素，第二个提出的主题旨在为iv）语音和v）神经生理学信号均开发下一代情境 - 感知的增强算法。最后，我们建议vi）构建一个完整的上下文感知的原型，该原型将语音，手势和身体用作HMI输入控制信号。将实施机器人控制应用程序中的验证实验，从而为长期应用打开门，例如远程医疗机器人，这些机器人意识到患者的精神状态，例如，可以检测抑郁症的发作并相应地采取必要的措施。 ***拟议的研究计划将重点放在对高素质人员（HQP）的出色培训上。 HQP将暴露于信号处理，认知工程，机器学习和动手原型的跨学科混合物，以及具有最先进的可穿戴技术和环境监测传感器的研究设施。此外，通过与行业的密切合作，HQP将配备当今竞争激烈的就业市场需求量高的技能和专业知识。 **