本文關鍵詞：基于運動傳感器的老年人活動智能識別與應用開發(fā)　出處：《重慶大學》2016年碩士論文　論文類型：學位論文

  更多相關文章： 活動識別 運動傳感器 特征提取 隱馬爾科夫模型 健康看護

【摘要】：隨著普適計算技術的不斷發(fā)展,人們對于舒適、安全、健康生活的需求也與日俱增�；谶\動傳感器技術的活動識別正在健康看護、人機交互、動作指導等領域發(fā)揮著重要的作用,同時活動識別也逐漸成為智能家居領域的一塊重要的拼圖�；顒渝憻捯殉蔀槔夏耆四承┞约膊〉妮o助治療手段之一,適當?shù)倪\動有益于提升身體機能及減緩老化程度。通過對老年人的活動進行識別,從而計算老年人的活動量,分析其活動規(guī)律,有針對性的增加或減少某項活動,這對于老年人的健康看護是具有重要意義的�；谟嬎銠C視覺和運動傳感器的活動識別是目前最為常見的兩種方法�；谟嬎銠C視覺的方法存在易受干擾、監(jiān)控范圍局限、侵犯隱私等主要缺陷,而基于傳感器的技術則具有抗干擾強、攜帶方便、數(shù)據(jù)獲取自由、保護隱私等優(yōu)點。本文提出了一種基于運動傳感器和HMM的活動識別方法。針對老年人的活動類型及活動特點提取了標準差、能量等用以區(qū)分靜態(tài)活動集合S中的活動,提取corr_VF、Amp、RAF(ratio forward)和RVF(ratio vertical forward)值用以區(qū)相似步態(tài)活動集D中的活動。在特征值提取后使用改進K均值算法生成HMM模型的觀測值集合,然后定義了本文的活動識別模型。在經(jīng)過Baum-Welch算法對HMM參數(shù)λ進行訓練后使用Viterbi算法來進行老年人的活動識別。通過對比實驗驗證了本文方法能有效應用于老年人的活動識別,對于連續(xù)單一活動的平均準確率達到93.4%,尤其是對于相似活動其平均識別率達到了93.7%;對于隨機組合序列活動的準確率達到91.1%。在實驗過程中本文還對比了不同傳感器佩戴部位對于活動識別精度的影響:在腰部、髖部等中樞部位佩戴傳感器進行活動識別會取得高于四肢的平均活動識別精度。本文設計了一種針對于老年人的日�；顒涌醋o系統(tǒng),該系統(tǒng)通過對活動識別記錄進行統(tǒng)計分析,為老年人的看護人員(親屬、醫(yī)護人員)提供圖形化的活動統(tǒng)計、分析信息,并為親屬提供有價值的有助于老年人的日常生活照顧的專家知識。該系統(tǒng)對于提升老年人身體健康水平、生活質(zhì)量具有重要意義。
[Abstract]:With the continuous development of pervasive computing technology, the demand for comfortable, safe and healthy life is increasing. Activity recognition based on motion sensor technology is playing an important role in health care, human-machine interaction, action guidance and other fields. Meanwhile, activity recognition has gradually become an important mosaic in the field of smart home. Exercise has become one of the auxiliary treatments for some chronic diseases of the elderly. Appropriate exercise is beneficial to improve the physical function and reduce the degree of aging. By identifying the activities of the elderly, we can calculate the activity of the elderly, analyze their activity rules, and increase or decrease a specific activity, which is significant for the elderly's health care. Activity recognition based on computer vision and motion sensor is the two most common method at present. Based on computer vision, there are main defects such as interference, limited monitoring range and privacy violation. Sensor based technology has the advantages of strong anti-interference, easy to carry, free data access, and privacy protection. In this paper, a motion recognition method based on motion sensor and HMM is proposed. According to the activity type and activity characteristics of the elderly, we extract standard deviation and energy to distinguish activities in static activity set S, extract corr_VF, Amp, RAF (ratio forward) and RVF (ratio vertical forward) values for activities in the similar gait activity set. After extracting the eigenvalues, the improved K mean algorithm is used to generate the set of observation values of the HMM model, and then the activity recognition model of this paper is defined. After training the HMM parameter by Baum-Welch algorithm, the Viterbi algorithm is used to identify the activities of the elderly. By comparing the experimental results indicate that this method can be used for identification of activities of the elderly, the average for the continuous single activity reach 93.4% accuracy rate, especially for the similar activities of the average recognition rate reached 93.7%; the accuracy rate for the random sequence of activities reached 91.1%. In the process of experiment, we also compared the influence of different sensors on the accuracy of activity recognition: wearing the sensor and identifying the activity in the waist, hip and other central parts would get the average activity recognition accuracy higher than the limbs. This paper introduces a design for the daily life of the elderly care system, this system through the statistical analysis of activity identification record for the care of elderly people (relatives, staff) provides a graphical statistical analysis, information activities, and provides expert knowledge of everyday life care for valuable help to the elderly the relatives. The system is of great significance to improve the health level and the quality of life of the elderly.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41;TP212

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