可穿戴生物医学系统中的机器学习

M. Chowdhury, A. Khandakar, Yazan Qiblawey, M. Reaz, Mohammad Tariqul Islam, F. Touati
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引用次数: 20

摘要

可穿戴技术通过对人体生理的实时连续监测,为医疗保健系统增加了一个全新的维度。它们用于日常活动和健康监测,甚至已渗透到监测慢性疾病患者的健康状况。为了开发更创新、更可靠的可穿戴设备,人们正在进行大量的研究和开发活动。本章将讨论不同应用的可穿戴设备的设计和实现,如心脏病发作的实时检测,异常心音,血压监测,糖尿病足监测的步态分析。本章还将介绍如何将从这些原型中获取的信号用于训练机器学习(ML)算法,以诊断佩戴该设备的人的状况。本章讨论了以下步骤:(i)硬件设计,包括传感器选择、表征、信号采集和与决策子系统的通信;(ii)机器学习算法设计,包括特征提取、特征约简、训练和测试。本章将以糖尿病足监测智能鞋垫、可穿戴式实时心脏病检测和智能数字听诊器系统的设计为例,展示可穿戴式生物医学系统的开发过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning in Wearable Biomedical Systems
Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems.
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