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

使用深度神经网络和广泛的 GPU 训练的复杂传感器的环境智能

• 批准号: RTI-2022-00522
• 负责人: Bouchard, Kévin
• 金额: $ 3.62万
• 依托单位: Université du Québec à Chicoutimi
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741823
• 关键词: Ambient; Intelligence; Complex; Sensors; using

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.

与西方世界的大部分地区类似，加拿大人口正在老龄化。这些人口变化产生了有据可查且众所周知的重大社会挑战。在这些挑战中，其中之一是尽可能长时间地让半自治人群和老年人留在家中。推迟将他们送入收容机构是一种更人道的解决方案，而且通常社会成本也较低。然而，在这方面，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。为了方便这些人的任务，智能家居在近几十年来已经出现，作为一种相关且廉价的替代方案来监控目标人的活动并确保他们的安全。尽管包括我们在内的世界各地的多个团队现在正在实际住宅中（与实验室相比）利用这些技术，但要充分发挥其潜力仍然存在许多挑战。事实上，大多数智能家居都依赖于简单的环境技术，例如运动探测器、电磁接触和环境传感器。这些类型的传感器只能提供墙壁之间发生的活动的粗略且不精确的图片。为了将活动识别提升到一个新的水平，必须利用在信息质量方面更具表现力的传感器。例如，我们团队在最近的项目中使用了超宽带雷达和热感摄像机。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。尽管许多简单的架构很容易在具有良好显卡的标准计算机上进行训练，但最先进的模型需要大量的计算能力和专用硬件来正确研究它们。因此，我们的团队目前的科学进步受到阻碍，因为它无法获得适当的资源。在此项目中，我们请求预算购买一台专门用于训练深度神经网络的机器，该机器配备 8 个 NVIDIA RTX A5000 GPU。这台计算机至关重要，将对我们的团队产生重大影响，更不用说为大学社区和当地企业带来巨大的好处。