Exploring the modelling of behaviour and context using deep learning under constrained computing platforms with applications to Digital Health

该项目属于ESPRC人工智能技术研究领域的福尔斯。这项研究的核心问题是，如何对消费者健康状况进行有效建模，从而利用数据挖掘方法确定用户行为和环境。从手机应用到可穿戴设备，如今的移动的传感形式通常只监测相对简单的行为和环境维度，例如睡眠时间和步数。然而，像深度学习这样的领域的进步表明，计算模型可以用于更复杂的现象（例如，用户情感、社会交互），在一定的鲁棒性水平上，它们在现实世界环境中是有用的。同时，受限设备的计算能力的进步（例如，低功耗GPU、小型硬件加速器）正在增加可在这些平台上执行的算法的复杂性。数据挖掘作为从大型数据集中提取有意义信息的有用过程具有广泛的应用。特别是，它在移动的设备上的健康数据建模中的应用引起了相当大的兴趣。这种兴趣主要来自软件和硬件的突破，即深度学习方法和设备计算能力。这项研究将涉及对当前模型的检查和随后的软件创新，以产生适用于受限计算平台的高效模型。目前使用的数据挖掘模型往往涉及性能和效率之间的权衡。一个谨慎的研究问题是解决算法冗余问题，并创新与可穿戴设备等受限计算平台相关的方法。通过该项目实现的主要目标包括但不限于以下内容：-使用深度学习原理和算法对受限平台的传感器数据进行建模，以便对用户行为和上下文的解释达到更大的广度和准确性;- 开发新的系统资源高效的深度学习方法，适用于受限的计算平台（如可穿戴设备和嵌入式平台）;- 通过软件/算法创新或新型硬件研究深度学习方法的潜在效率提升;该研究的新奇在于可能通过试验不同的机器学习架构而产生的潜在解决方案。最后，该项目还符合EPSRC在提供智能技术和系统方面的战略。该项目还坚持更广泛的跨信通技术优先事项，因为它寻求研究真实的医疗保健数据，因此该项目以信通技术为中心，但不一定只与信通技术有关。

This project falls within the ESPRC Artificial Intelligence Technologies research area. The central question of the study is the effective modelling of consumer health to determine user behaviour and context using data-mining methods.Today's forms of mobile sensing, ranging from phone apps to wearable devices, typically monitor relatively simple dimensions of behaviour and context; for instance, sleep duration and step counts. However, advances in areas like deep learning are demonstrating computational models are possible for much more complex phenomena (e.g., user emotion, social interactions), at a level of robustness that they can be useful in real-world environments. Simultaneously, advances in the computational power of constrained devices (e.g., low-power GPUs, small-form-factor hardware accelerators) are increasing the sophistication of algorithms that are feasible to execute on these platforms.Data mining has widespread applications as a useful process for extracting meaningful information from large datasets. In particular, its application in the modelling of health data on mobile devices has generated considerable interest. Such interest is chiefly motivated by breakthroughs in both software and hardware, namely deep learning methods and device computational power.This research will involve an examination of current models and a subsequent software innovation to produce efficient models suited for constrained computing platforms. Current usage of data mining models often involves a trade-off between performance and efficiency. A prudent research question would be to tackle algorithmic redundancies and innovate for methods with relevance to constrained computing platforms such as wearables.The main objectives to be achieved through this project include, but are not limited to, the following:- modelling sensor data from constrained platforms using deep learning principles andalgorithms such that the interpretation of user behaviour and context reaches greater breadth and accuracy;- developing new system resource-efficient deep learning methods suited to constrained computing platforms (such as wearable devices and embedded platforms);- investigating potential efficiency gains in deep learning methods through software/algorithmic innovation or novel hardware/processor directions.The novelty of the research lies on the potential solutions that might result from experimenting with varying machine learning architectures. Finally, this project also aligns to EPSRC's strategy in delivering intelligent technologies and systems. The project also adheres to broader Cross-ICT priorities since it seeks to look at real healthcare data, such that the project is ICT-centric but not necessarily solely related to ICT.