Investigate how to design cost-effective wearable intelligence techniques with dynamic active learning algorithms

研究如何设计具有成本效益的可穿戴智能技术和动态主动学习算法，以客观量化和敏感地测量自由生活环境中个人的体力活动(PA)行为。为了遏制全球范围内缺乏体力活动的流行和相关的平均每年530万人死亡，世界卫生组织(WHO)的报告证明了体力活动(PA)研究的重要性。英国2017年的估计显示，超过四分之一的16岁及以上的人被归类为身体不活跃。研究表明，可穿戴设备和智能手机的普及使人们能够毫不费力地跟踪和管理日常PA，并有可能改善他们的健康状况。但由于缺乏高性价比的传感器、非标准化的基线数据集、有效和高效的学习算法等原因，这些可穿戴技术在自由生活环境中客观限定PA的准确性和健壮性较弱。谢菲尔德大学和蚂蚁数据有限公司将通过重要的联合研究和开发来检验学术界的技术发展，该研究将研究如何将先进的机器学习技术无缝集成到可穿戴系统中，以客观限定PA行为。该研究旨在产生现实生活中可穿戴的PA基线数据集、机器学习模型和统一的期刊论文。主要目的是研究如何设计具有成本效益的可穿戴智能技术和动态主动学习算法，以客观量化和敏感地测量自由生活环境中的个人身体活动(PA)行为。该项目涉及三个关键领域：i)缺乏全面的调查，以使用在自由生活环境中收集个人PA数据的具有成本效益的可穿戴技术来建立标准化的PA行为基线数据集；ii)缺乏有效的特征选择技术来从未标记和不确定的PA数据中提取可靠的特征；以及iii)缺乏有用的机器学习技术来提高PA行为资格鉴定的准确性和稳健性。

investigate how to design cost-effective wearable intelligence techniques with dynamic active learning algorithms to objective quantification and sensitive measure of personal physical activity (PA) behaviour in free-living environments.For curbing the global prevalence of physical inactivity and the associated average of 5.3 million deaths per year, the importance of the Physical Activity (PA) research has been demonstrated by World Health Organisation (WHO) reports. UK estimates in 2017 suggest over a quarter of people aged 16 years and over are categorised as physically inactive'. Research has shown that the prevalence of wearable devices and smartphones enable people to track and manage daily PA effortlessly, and potentially improve their health outcomes. But these wearable technologies suffer from low accuracy and weak robustness of objectively qualifying PA in free-living environments due to shortage of cost-effective sensors, unstandardised baseline dataset, effective and efficient learning algorithms, etc. Technological developments in the academic community will be examined through significant joint research and development between the University of Sheffield and Ant-Data Ltd that will examine how to seamlessly integrate advanced machine learning techniques into wearable systems for objective qualification of PA behaviour.The research aims to result in a real-life wearable PA baseline dataset, machine learning models and aligned journal papers. The main aim is to investigate how to design cost-effective wearable intelligence techniques with dynamic active learning algorithms to objective quantification and sensitive measure of personal physical activity (PA) behaviour in free-living environments. This project addresses three key areas: i) Lack of holistic investigations on establishing standardised PA behaviour baseline datasets using cost-effective wearable technologies that collect personal PA data in free-living environments; ii) Lack of effective feature selection techniques for extracting reliable features from unlabelled and uncertain PA data, and iii) Lack of useful machine learning techniques for improving accuracy and robustness of qualification of PA behaviour.