老年人摔倒檢測與預(yù)警系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)
發(fā)布時(shí)間：2018-07-17 06:38
【摘要】：摔倒已經(jīng)成為目前最主要的導(dǎo)致老年人受傷以及住院的意外事件。對老年人摔倒事件的及時(shí)發(fā)現(xiàn)能有效的降低老年人受到的損傷。由于智能手機(jī)的普適性,基于智能手機(jī)的摔倒檢測與預(yù)警系統(tǒng)成為近些年來的研究熱點(diǎn),但是以往的研究往往只利用了加速計(jì)一個(gè)手機(jī)元件來作為摔倒檢測的依據(jù),因此如何充分利用智能手機(jī)上已有的傳感器來設(shè)計(jì)一個(gè)高檢測率、低誤報(bào)率的系統(tǒng)是本文的研究重點(diǎn)。本文的研究目的在于利用從智能手機(jī)及智能手表提取出的設(shè)備姿態(tài)數(shù)據(jù)、相對高度數(shù)據(jù)、心率數(shù)據(jù)等設(shè)計(jì)一個(gè)高準(zhǔn)確率、低誤報(bào)率以及多預(yù)警等級的摔倒檢測與預(yù)警算法,并采取多種預(yù)警方式來向看護(hù)人員發(fā)送救護(hù)預(yù)警信息,最后設(shè)計(jì)和實(shí)現(xiàn)了一個(gè)較為完備的老年人摔倒檢測與預(yù)警系統(tǒng)。首先,為了利用智能手機(jī)判定老年人摔倒事件的發(fā)生,對基于智能手機(jī)的摔倒檢測方法及預(yù)警方式進(jìn)行研究,提出本文采取的摔倒事件檢測思想,明確系統(tǒng)相關(guān)使用角色。在此基礎(chǔ)上對老年人摔倒檢測與預(yù)警系統(tǒng)進(jìn)行需求分析,確定系統(tǒng)必備的功能需求、核心業(yè)務(wù)及系統(tǒng)的工作流程。其次,為了提高摔倒檢測的準(zhǔn)確率,本文基于DeviceMotion提出一種描述不同運(yùn)動(dòng)事件的特征向量,為了驗(yàn)證4種經(jīng)典分類算法基于該特征向量檢測摔倒事件的可靠性,本文邀請志愿者佩戴有安裝運(yùn)動(dòng)數(shù)據(jù)采集程序的手機(jī)進(jìn)行模擬摔倒試驗(yàn)以及每日的正�；顒�(dòng),基于構(gòu)造的樣本集對4種分類算法的性能表現(xiàn)進(jìn)行了分析對比實(shí)驗(yàn)。再次,對老年人摔倒檢測與預(yù)警系統(tǒng)進(jìn)行了設(shè)計(jì)工作。遵循軟件工程的思想對系統(tǒng)進(jìn)行了設(shè)計(jì),并在摔倒檢測與預(yù)警模塊中采用震動(dòng)提醒、鈴聲提醒以及語音識別三種人機(jī)交互方式來降低摔倒事件的誤報(bào)率。最后,實(shí)現(xiàn)并測試了老年人摔倒檢測與預(yù)警系統(tǒng)。本文通過Core Motion框架來獲取老年人的運(yùn)動(dòng)數(shù)據(jù),通過Health Kit框架來獲取老年人的心率數(shù)據(jù),通過科大訊飛開源框架來實(shí)現(xiàn)語音識別功能,通過Core Location框架來獲取老年人的位置信息,通過Cloud Kit來發(fā)送老年人的救護(hù)預(yù)警信息。最后對系統(tǒng)進(jìn)行了功能和非功能測試。
[Abstract]:Fall has become the main cause of the injury to the elderly and the accident in hospital. The timely discovery of the fall of the elderly can effectively reduce the damage to the elderly. Because of the universality of the smart phone, the detection and early warning system based on the smartphones has become a hot research topic in recent years, but the previous research has been studied. The focus of this paper is how to make full use of the existing sensors on the smart phone to design a high detection rate and low false alarm rate. The purpose of this paper is to make use of the device posture extracted from smart phone and smart watch. The data, the relative height data, the heart rate data and so on, design a high accuracy rate, low false alarm rate and the multiple warning level fall detection and early warning algorithm, and take a variety of early warning methods to send the nursing early warning information to the caregivers, and finally design and implement a relatively complete detection and early warning system for the elderly fall. Using smart phone to determine the occurrence of falling events of the elderly, research on the detection methods and early warning methods based on the smartphone, put forward the idea of the fall event detection and clarify the related role of the system. On this basis, the needs of the elderly fall detection and early warning system are analyzed, and the necessary functions of the system are determined. Requirements, core business and the workflow of the system. Secondly, in order to improve the accuracy of the fall detection, this paper presents a feature vector describing different events based on DeviceMotion. In order to verify the reliability of the 4 classical classification algorithms based on the feature vector detection, this article invites the volunteers to wear the installed sports data. The handset of the acquisition program carries on the simulated fall test and the daily normal activities. Based on the structural sample set, the performance of the 4 classification algorithms is analyzed and contrasted. Thirdly, the design work of the elderly fall detection and early warning system is carried out. The system is designed and the fall detection is carried out following the thinking of software engineering. In the early warning module, three human computer interaction methods are used to reduce the false alarm rate of the fall events. Finally, the old people's fall detection and early warning system is realized and tested. In this paper, the Core Motion framework is used to obtain the motion data of the elderly, and the heart rate of the elderly is obtained through the Health Kit framework. The data, through the open source framework of the HKDA to realize the voice recognition function, through the Core Location framework to obtain the location information of the elderly, through the Cloud Kit to send the elderly ambulance early warning information. Finally, the system has been tested for functional and non functional.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.52;TP277
[Abstract]:Fall has become the main cause of the injury to the elderly and the accident in hospital. The timely discovery of the fall of the elderly can effectively reduce the damage to the elderly. Because of the universality of the smart phone, the detection and early warning system based on the smartphones has become a hot research topic in recent years, but the previous research has been studied. The focus of this paper is how to make full use of the existing sensors on the smart phone to design a high detection rate and low false alarm rate. The purpose of this paper is to make use of the device posture extracted from smart phone and smart watch. The data, the relative height data, the heart rate data and so on, design a high accuracy rate, low false alarm rate and the multiple warning level fall detection and early warning algorithm, and take a variety of early warning methods to send the nursing early warning information to the caregivers, and finally design and implement a relatively complete detection and early warning system for the elderly fall. Using smart phone to determine the occurrence of falling events of the elderly, research on the detection methods and early warning methods based on the smartphone, put forward the idea of the fall event detection and clarify the related role of the system. On this basis, the needs of the elderly fall detection and early warning system are analyzed, and the necessary functions of the system are determined. Requirements, core business and the workflow of the system. Secondly, in order to improve the accuracy of the fall detection, this paper presents a feature vector describing different events based on DeviceMotion. In order to verify the reliability of the 4 classical classification algorithms based on the feature vector detection, this article invites the volunteers to wear the installed sports data. The handset of the acquisition program carries on the simulated fall test and the daily normal activities. Based on the structural sample set, the performance of the 4 classification algorithms is analyzed and contrasted. Thirdly, the design work of the elderly fall detection and early warning system is carried out. The system is designed and the fall detection is carried out following the thinking of software engineering. In the early warning module, three human computer interaction methods are used to reduce the false alarm rate of the fall events. Finally, the old people's fall detection and early warning system is realized and tested. In this paper, the Core Motion framework is used to obtain the motion data of the elderly, and the heart rate of the elderly is obtained through the Health Kit framework. The data, through the open source framework of the HKDA to realize the voice recognition function, through the Core Location framework to obtain the location information of the elderly, through the Cloud Kit to send the elderly ambulance early warning information. Finally, the system has been tested for functional and non functional.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.52;TP277
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