【摘要】：我國是世界上人口最多的國家,近幾年我國的老年人口逐漸增多,并向老齡化社會邁進。而老年人由于自身生理因素的影響,身體機能不斷衰減,在日常活動中往往會遇到一些麻煩,甚至是突發(fā)狀況如摔倒、中風(fēng)等,如果發(fā)生這些異常的情況而得不到及時救助的話,將會發(fā)生不可挽回的悲劇。因此解決老年人的行動安全和醫(yī)療保健問題逐漸成為熱門的研究領(lǐng)域�；谝陨媳尘把芯坎⒃O(shè)計了一種移動健康監(jiān)護跟蹤系統(tǒng)。本文主要研究內(nèi)容如下:首先,從動力學(xué)的角度對人體日�；顒雍退さ惯^程進行了受力分析,根據(jù)人體日�；顒雍退さ箷r的受力差別,分析了SVM閾值檢測算法。應(yīng)用小波變換對心電數(shù)據(jù)進行去噪,再利用心電信號數(shù)據(jù)庫MIT-BIH中的心電信號數(shù)據(jù)進行去噪仿真實驗。其次,根據(jù)現(xiàn)有的摔倒檢測算法和心電信號特征值提取算法,提出了一種基于小波變換的心電信號摔倒檢測算法。單片機利用SVM閾值檢測算法判斷人體疑似摔倒,若人體為疑似摔倒狀態(tài),就給手機發(fā)送FF信號;手機根據(jù)FF信號開始接收心電信號數(shù)據(jù),利用小波變換對心電信號數(shù)據(jù)進行特征提取;利用正常心電信號特征值和疑似摔倒時心電信號的特征值進行對比分析,進而對人體摔倒狀態(tài)進行判定。再次,完成了移動健康監(jiān)護跟蹤系統(tǒng)的設(shè)計,包括硬件和軟件兩部分。硬件部分主要包括心電信號采集模塊、三維變量采集模塊和藍牙模塊,其中心電信號采集模塊采集人體的心率數(shù)據(jù),三維變量采集模塊采集人體三個軸向的加速度,藍牙模塊實現(xiàn)硬件與手機的數(shù)據(jù)通信。軟件部分主要包括心電信號數(shù)據(jù)接收模塊、心電波形顯示模塊、心電信號特征值提取模塊、心電信號摔倒檢測模塊、GPS地理位置獲取模塊以及短信示警模塊。最后,對系統(tǒng)各個模塊進行了實驗測試,并對系統(tǒng)進行人體摔倒測試和人體不同運動狀態(tài)誤報測試。實驗結(jié)果表明,本系統(tǒng)能夠有效的判斷出人體是否為摔倒狀態(tài),且具有實時性和便攜性,但不適合對正在進行劇烈活動的人體進行摔倒檢測。
[Abstract]:China is the most populous country in the world. In recent years, the elderly population in China has gradually increased and is moving forward to an aging society. However, due to the influence of their own physiological factors, the physical function of the elderly continues to decline, and they often encounter some problems in their daily activities, even sudden situations such as falls, strokes, and so on. If these abnormal situations are not rescued in a timely manner, irreparable tragedies will occur. Therefore, solving the problem of mobile safety and health care for the elderly has gradually become a hot research field. Based on the above background, a mobile health monitoring and tracking system is designed. The main contents of this paper are as follows: firstly, the stress analysis of human daily activity and fall process is carried out from the point of view of dynamics, and the SVM threshold detection algorithm is analyzed according to the difference of human daily activity and falling force. Wavelet transform is used to Denoise ECG data, and then ECG data in ECG database MIT-BIH are used to Denoise simulation experiment. Secondly, according to the existing fall detection algorithm and ECG signal eigenvalue extraction algorithm, an ECG fall detection algorithm based on wavelet transform is proposed. Single chip microcomputer uses SVM threshold detection algorithm to judge the suspected fall of the human body, if the human body is a suspected fall state, it will send the FF signal to the mobile phone. According to the FF signal, the mobile phone begins to receive ECG signal data, and wavelet transform is used to extract the ECG signal data. The eigenvalues of normal ECG signals and suspected falls are compared and analyzed, and then the falling state of human body is judged. Thirdly, the design of mobile health monitoring and tracking system is completed, including hardware and software. The hardware part mainly includes ECG signal acquisition module, three-dimensional variable acquisition module and Bluetooth module. Its central electrical signal acquisition module collects human heart rate data, and three-dimensional variable acquisition module collects three axial acceleration of human body. Bluetooth module realizes the data communication between hardware and mobile phone. The software mainly includes ECG data receiving module, ECG waveform display module, ECG eigenvalue extraction module, ECG fall detection module, GPS geographical location acquisition module and SMS alarm module. Finally, the experimental tests are carried out on each module of the system, and the human fall test and the false alarm test of different motion states of the system are carried out. The experimental results show that the system can effectively judge whether the human body is a fall state, and has real-time and portability, but it is not suitable for the fall detection of the human body who is engaged in strenuous activity.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP274

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