Emotion-aware assistive technologies in rehabilitation and healthcare

越来越需要技术来帮助照顾加拿大不断增加的残疾人和老龄化人口。当前技术的一个局限性是，它们不能像人类护理人员那样识别或响应情绪。预防意识将提高辅助技术解释和适应用户需求的能力。例如，对急性恐惧的检测可以提高独居老人跌倒检测器的准确性;对挫折或参与的意识可以用于优化虚拟学习和康复的电脑游戏;对临床重要情绪的警觉可以促进慢性疾病（例如抑郁症）的自我管理。许多研究表明，在实验室条件下，可以通过观察心率和呼吸等多种生理信号来检测情绪。然而，在日常消费品能够可靠地检测情绪之前，需要科学和工程的进步。本研究的目的是：（1）开发能够适应不同人的不同生理和情绪反应的情绪检测器;（2）通过结合生理，上下文（例如一天中的时间）和心理（例如个性特质）的情绪指标来提高检测准确性;（3）创建一个原型的情绪感知计算机学习游戏，并比较用户的表现和游戏的享受与没有情绪感知。为了实现这些目标，我们将收集参与者的情绪，生理，心理和上下文数据，因为他们参与不同难度和持续时间的电脑游戏。我们将引出与学习体验相关的积极（参与，享受）和消极（挫折，焦虑）情绪。我们将开发模型来使用收集的数据对所经历的情绪进行分类。我们将在不同的时间为不同的人测试我们的模型的准确性。这项研究将最终导致自闭症儿童的计算机学习游戏的开发，以改善获得和降低与他们的护理相关的强化治疗的成本。我们的研究广泛适用于工作场所压力管理，智能家居和健康监测的商业机会。

There is a growing need for technologies to help care for Canada's increasing disabled & aging populations. One limitation of current technologies is that they cannot recognize or respond to emotions like human caregivers do. Emotion-awareness would enhance the ability of assistive technologies to interpret & adapt to user needs. For example, detection of acute fear could improve the accuracy of fall detectors for elders living alone; awareness of frustration or engagement could be used to optimize computer games for virtual learning & rehabilitation; alertness to clinically important emotions could facilitate self-management of chronic conditions (e.g. depression). Many studies have shown that emotions can be detected by observing multiple physiological signals like heart rate & respiration in laboratory conditions. However, scientific & engineering advancements are needed before emotions can be reliably detected by everyday consumer products. The objectives of this research are:(1) To develop emotion detectors that can accommodate diverse physiological & emotional responses of different people; (2) To improve detection accuracy by combining physiological, contextual (e.g. time of day) & psychological (e.g. personality traits) indicators of emotions, & (3) To create a prototype emotion-aware computer learning game and compare user performance and enjoyment of the game with and without emotion-awareness. To achieve these objectives, we will collect emotional, physiological, psychological, & contextual data from participants as they engage in computer games that vary in difficulty & duration. We will elicit positive (engagement, enjoyment) & negative (frustration, anxiety) emotions related to the learning experience. We will develop models to classify the emotions experienced using the collected data. We will test the accuracy of our models for different people at different times. This research will eventually lead to the development of computer learning games for autistic children to improve access to and reduce costs of intensive therapies associated with their care. Our research is widely applicable with commercial opportunities in workplace stress management, smart homes & health monitoring.