老年人摔倒檢測與預警系統(tǒng)的設計與實現(xiàn)
發(fā)布時間：2018-07-17 06:38
【摘要】：摔倒已經成為目前最主要的導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發(fā)現(xiàn)能有效的降低老年人受到的損傷。由于智能手機的普適性,基于智能手機的摔倒檢測與預警系統(tǒng)成為近些年來的研究熱點,但是以往的研究往往只利用了加速計一個手機元件來作為摔倒檢測的依據,因此如何充分利用智能手機上已有的傳感器來設計一個高檢測率、低誤報率的系統(tǒng)是本文的研究重點。本文的研究目的在于利用從智能手機及智能手表提取出的設備姿態(tài)數據、相對高度數據、心率數據等設計一個高準確率、低誤報率以及多預警等級的摔倒檢測與預警算法,并采取多種預警方式來向看護人員發(fā)送救護預警信息,最后設計和實現(xiàn)了一個較為完備的老年人摔倒檢測與預警系統(tǒng)。首先,為了利用智能手機判定老年人摔倒事件的發(fā)生,對基于智能手機的摔倒檢測方法及預警方式進行研究,提出本文采取的摔倒事件檢測思想,明確系統(tǒng)相關使用角色。在此基礎上對老年人摔倒檢測與預警系統(tǒng)進行需求分析,確定系統(tǒng)必備的功能需求、核心業(yè)務及系統(tǒng)的工作流程。其次,為了提高摔倒檢測的準確率,本文基于DeviceMotion提出一種描述不同運動事件的特征向量,為了驗證4種經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動數據采集程序的手機進行模擬摔倒試驗以及每日的正�；顒�,基于構造的樣本集對4種分類算法的性能表現(xiàn)進行了分析對比實驗。再次,對老年人摔倒檢測與預警系統(tǒng)進行了設計工作。遵循軟件工程的思想對系統(tǒng)進行了設計,并在摔倒檢測與預警模塊中采用震動提醒、鈴聲提醒以及語音識別三種人機交互方式來降低摔倒事件的誤報率。最后,實現(xiàn)并測試了老年人摔倒檢測與預警系統(tǒng)。本文通過Core Motion框架來獲取老年人的運動數據,通過Health Kit框架來獲取老年人的心率數據,通過科大訊飛開源框架來實現(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)送老年人的救護預警信息。最后對系統(tǒng)進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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.52;TP277
【參考文獻】
中國期刊全文數據庫 前4條
1 耳玉亮;段蕾蕾;葉鵬鵬;汪媛;紀翠蓉;鄧曉;高欣;金葉;王臨虹;;2014年全國傷害監(jiān)測系統(tǒng)老年跌倒/墜落病例特征分析[J];中華流行病學雜志;2016年01期
2 賈菲菲;賓光宇;吳水才;師玉濤;;基于加速度信號的縱跳力學參數計算[J];中國醫(yī)療設備;2015年09期
3 王志寶;孫鐵山;張杰斐;;人口老齡化區(qū)域類型劃分與區(qū)域演變分析——以中美日韓四國為例[J];地理科學;2015年07期
4 曹玉珍;蔡偉超;程e，
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