Ambient Intelligence from Complex Sensors using Deep Neural Networks and Extensive GPU Training

加拿大的人口正在老龄化，这与西方世界的许多国家相似。这些人口结构的变化产生了重大的社会挑战，这是有据可查的，也是众所周知的。在这些挑战中，其中之一是尽可能长时间地让半自治人口和老年人留在家里。推迟他们的制度化是一种更人道的解决方案，通常对社会来说代价更低。然而，在这方面，来自进行家访的护理人员或医疗保健专业人员的支持变得至关重要。为了方便这些人的工作，近几十年来，智能家居作为一种相关且廉价的替代方案出现，用于监控目标人群的活动并确保他们的安全。尽管世界各地的几个团队，包括我们的团队，现在正在真实的住宅中利用这些技术(与实验室相比)，但要充分发挥它们的潜力仍然存在许多挑战。事实上，大多数智能家居依赖于简单的环境技术，如运动探测器、电磁触点和环境传感器。这些类型的传感器只能提供墙壁之间发生的活动的粗略和不精确的图像。为了将活动识别提升到一个新的水平，开发在信息质量方面更具表现力的传感器变得势在必行。例如，我们的团队在最近的项目中使用了超宽带雷达和热像仪。这些技术允许收集更复杂的数据，但仍然保护居民的隐私，因为数据无法识别。然而，为了充分利用这些技术，有必要使用复杂的深度学习模型。尽管许多简单的体系结构很容易在具有良好显卡的标准计算机上进行培训，但最先进的模型需要大量的计算能力和专门的硬件来适当地研究它们。因此，我们的团队目前在科学进步方面受到阻碍，因为它无法获得适当的资源。在这个项目中，我们要求预算购买一台配备8个NVIDIA RTX A5000图形处理器的专门用于训练深度神经网络的机器。这台计算机是必不可少的，将对我们的团队产生重大影响，更不用说给大学社区和当地企业带来的巨大好处了。

The Canadian population is aging similarly to much of the Western world. These demographic changes generate significant societal challenges that are well-documented and known. Among these challenges, one of them is to maintain semi-autonomous populations and the elderly at home for as long as possible. Delaying their institutionalization is a more humane solution and is generally less costly for society. However, in that regard, support from caregivers or healthcare professionals who make home visits becomes crucial. In order to facilitate the task of these persons, smart homes have emerged in recent decades as a relevant and inexpensive alternative to monitor the target people's activities and ensure their safety. Although several teams around the world, including ours, are now exploiting these technologies in real residences (compared to laboratories), there are still many challenges to achieve their full potential. Indeed, most smart homes rely on simple ambient technologies such as motion detectors, electromagnetic contacts, and environmental sensors. These types of sensors only provide a crude and imprecise picture of the activities going on between the walls. In order to take activity recognition to the next level, it becomes imperative to exploit sensors that are more expressive in terms of information quality. For example, our team has used ultra-wideband radars and thermal cameras in recent projects. These technologies allow more complex data to be collected, but still protect the privacy of residents because the data is not identifying. However, to fully take advantage of these technologies, it becomes necessary to use complex deep learning models. Although many simple architectures are easily trainable on standard computers with good graphics cards, state-of-the-art models require a lot of computing power and specialized hardware to properly investigate them. Thus, our team is currently hampered in its scientific progress because it does not have access to the appropriate resources. In this project, we are requesting a budget to acquire a machine dedicated to training deep neural networks equipped with 8 NVIDIA RTX A5000 GPUs. This computer is essential and will have a significant impact on our team, not to mention the huge benefits for the university community and local businesses.