Radar micro-Doppler for healthcare applications

用于医疗保健应用的雷达微多普勒

• 批准号: 1805516
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1805516
• 关键词: Radar; micro; Doppler; healthcare; applications

The context of this research is the use of radar systems for indoor monitoring of people aimed at detecting patterns of life to infer information regarding the health status of vulnerable people such as elderly and people with cognitive or physical disabilities. Radar can provide a contactless and unobtrusive tool to perform this monitoring, with no need of using invasive cameras or wearable devices.Micro-Doppler indicates the small modulations on the radar echoes generated by movements and rotations of various parts of a moving target, and in case of humans these are related to the small swinging movements of limbs and torso oscillations while an individual is moving. Human micro-Doppler signatures extracted from radar sensors have been investigated in recent years for a variety of applications, such as classification of different activities performed by people (walking, running, carrying objects) recognition of individuals based on their walking gait, and healthcare/assistive purposes (e.g. early detection of fall events involving elderly people, or gait characterisation in the presence of walking assistive devices). Several signal processing methods have been proposed in the literature to characterise human micro-Doppler signatures for healthcare, as well as many different features that can be extracted and processed by machine learning algorithms to perform automatic recognition and classification. In this PhD project the candidate will perform a mix of experimental work collecting data using radar systems in realistic healthcare and indoor monitoring scenarios, as well as analysis of these data investigating different time-frequency distributions to characterise micro-Doppler signatures, different feature extraction and selection techniques, and different algorithms based on machine learning to optimise the classification performance. During this project the candidate is expected to develop the expertise to lead his/her experimental research project, to become familiar with both hardware and signal processing aspects related to radar & sensing, and to interact effectively with colleagues from different backgrounds and disciplines.There is significant scope for a detailed investigation of many aspects and approaches to improve the overall system performance, in particular the identification and selection of the most suitable features and the fusion of information from multiple radar sensors or from different types of sensors, which are expected to be more and more present in indoor environments with the development of Internet of Things technologies. There is interest in characterising the normal walking gait prior to a fall event, especially when assistive walking devices such as canes/walkers are used, in reducing false alarms related to actions such as picking up objects from the floor, and in testing algorithms in realistic experimental indoor scenarios with multiple sensors. There is interest in applying novel deep learning techniques for improved classification of indoor movements and patterns of life.These reliable monitoring systems can be beneficial not only for fall detection, but also to evaluate more generically the pattern of life of an individual, for instance how active the person is, how often he/she moves in different part of the house and what activities are performed, in particular fundamental activities such as food intake. Irregularities with respect to the normal pattern of life of a person can be used for early detection of deteriorating health conditions (for instance initial symptoms of dementia), providing the opportunity for timely and more effective treatment.This project is believed to fit with the EPSRC 'Healthy nation' objective, in particular the ambitions 'transform community health & care' and 'improve prevention & public health'.

这项研究的背景是使用雷达系统对人们进行室内监测，旨在检测生活模式，以推断有关老年人和认知或身体残疾人等弱势群体健康状况的信息。雷达可以提供一种非接触和不显眼的工具来执行这种监测，而不需要使用侵入式摄像机或可穿戴设备。微多普勒表示移动目标的各个部分的运动和旋转产生的雷达回波的微小调制，对于人类来说，这些调制与个体移动时四肢的微小摆动和躯干振荡有关。近年来，从雷达传感器提取的人类微多普勒特征已被研究用于各种应用，例如对人进行的不同活动（步行、跑步、携带物体）进行分类，基于其步行步态识别个体，以及医疗保健/辅助目的（例如，涉及老年人的跌倒事件的早期检测，或在存在步行辅助设备的情况下的步态表征）。文献中已经提出了几种信号处理方法来识别用于医疗保健的人体微多普勒特征，以及可以通过机器学习算法提取和处理以执行自动识别和分类的许多不同特征。在这个博士项目中，候选人将执行一系列实验工作，在现实的医疗保健和室内监测场景中使用雷达系统收集数据，以及分析这些数据，调查不同的时间-频率分布以识别微多普勒特征，不同的特征提取和选择技术，以及基于机器学习的不同算法，以优化分类性能。在这个项目中，候选人将发展专业知识来领导他/她的实验研究项目，熟悉与雷达和传感相关的硬件和信号处理方面，并与来自不同背景和学科的同事进行有效的互动。有很多方面和方法的详细调查，以提高整体系统性能的重要范围，特别是识别和选择最合适的特征以及融合来自多个雷达传感器或来自不同类型传感器的信息，随着物联网技术的发展，这些技术预计将越来越多地出现在室内环境中。人们对在跌倒事件之前表征正常行走步态感兴趣，特别是当使用诸如手杖/助行器之类的辅助行走设备时，减少与诸如从地板上捡起物体之类的动作相关的假警报，以及在具有多个传感器的现实实验室内场景中测试算法。有兴趣应用新的深度学习技术来改进室内运动和生活模式的分类。这些可靠的监测系统不仅有利于跌倒检测，而且还可以更一般地评估个人的生活模式，例如人有多活跃，他/她在房子的不同部分移动的频率以及进行了什么活动，特别是食物摄入等基本活动。对一个人的正常生活模式的不满可以用于早期发现恶化的健康状况（例如痴呆症的初始症状），提供及时和更有效的治疗机会。该项目被认为符合EPSRC“健康国家”的目标，特别是“改变社区卫生和护理”和“改善预防和公共卫生”的目标。

项目成果

Feature diversity for fall detection and human indoor activities classification using radar systems

• DOI: 10.1049/cp.2017.0381
• 发表时间: 2017
• 作者: Aman Shrestha;J. Kernec;F. Fioranelli;Enea Cippitelli;E. Gambi;S. Spinsante

Bi-LSTM Network for Multimodal Continuous Human Activity Recognition and Fall Detection

• DOI: 10.1109/jsen.2019.2946095
• 发表时间: 2020-02-01
• 期刊: IEEE SENSORS JOURNAL
• 影响因子: 4.3
• 作者: Li, Haobo;Shrestha, Aman;Fioranelli, Francesco
• 通讯作者: Fioranelli, Francesco

Gait analysis of horses for lameness detection with radar sensors

使用雷达传感器对马匹进行步态分析以进行跛行检测

• DOI: 10.1049/cp.2017.0427
• 发表时间: 2017
• 作者: Shrestha A

A Multisensory Approach for Remote Health Monitoring of Older People

• DOI: 10.1109/jerm.2018.2827099
• 发表时间: 2018-06-01
• 期刊: IEEE JOURNAL OF ELECTROMAGNETICS RF AND MICROWAVES IN MEDICINE AND BIOLOGY
• 影响因子: 3.2
• 作者: Li, Haobo;Shrestha, Aman;Fioranelli, Francesco
• 通讯作者: Fioranelli, Francesco

Cross-Frequency Classification of Indoor Activities with DNN Transfer Learning

• DOI: 10.1109/radar.2019.8835844
• 发表时间: 2019-01
• 期刊: 2019 IEEE Radar Conference (RadarConf)
• 作者: Aman Shrestha;Corben Murphy;Ivan Johnson;Ajaymehul Anbulselvam;F. Fioranelli;J. Le Kernec;S. Gurbuz