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論文名稱(外文):Development and Research of Disability Care Assistant Management System
外文關鍵詞:artificial neural networkfall detectionaccelerometerLoRadisability care
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本研究符合現行智慧手環趨勢,將微控單元、加速度計及LoRa無線網路模組整合配戴於手腕部位,透過LoRa無線網路收集前、後、左、右跌倒等4種跌倒行為數據,並提出訊號向量強度(Siginal Vector Magnitude, SVM)單閥值方法結合動態視窗法(Dynamic Window Approach, DWA)等方法取出加速度計跌倒及日常生活活動特徵資訊後,進而利用倒傳遞類神經網路(Back Propagation Neural Network,BPNN)演算法進行學習,輔以走路、坐下、起立、上樓、下樓等5個日常生活活動(Activities of Daily Living, ADL)驗證跌倒事件判斷準確度,實驗證明使用BPNN分類方法可成功實現辨識跌倒行為之發生,本方法離線測試跌倒發生準確度100%,實驗中亦考量不同年齡族群ADL行為,驗證本系統在不同年齡層做跌倒判斷也可適用,在實時檢測時,亦可獲得100%跌倒靈敏度,所有跌倒行為均可被偵測及0.8%的誤報率,在系統運作過程中不會因誤報造成生活上多餘的煩腦,這是令人滿意的誤報水準;最後跌倒發生時將透過乙太網路經由4G/Wi-Fi將跌倒告警傳送至個人電腦或行動裝置,使照護者能即時提供醫療救護,降低跌倒造成的傷害。

In view of the year-on-year increase in the number of elderly people, Taiwan has officially entered the advanced society in 2018. The slow response to growth with age is often accompanied by many chronic diseases. These factors are particularly prone to falls, and falls often have irreversible consequences, resulting in families and The society must bear the burden of losing loved ones or disability care, so detecting the occurrence of falls and providing timely medical assistance to alleviate the damage caused by falls has become an important issue”.
This study is in line with the current trend of smart bracelets. It integrates the micro-control unit, accelerometer and LoRa wireless network module into the wrist, and collects four kinds of fall behavior data such as front, back, left and right falls through the LoRa wireless network. And the Siginal Vector Magnitude (SVM) single threshold combined with the Dynamic Window Approach (DWA) method to extract the accelerometer fall and daily life behavior characteristics information, and then use the inverse transfer neural network Back Propagation Neural Network (BPNN) algorithm learning, supplemented by walking, sitting, standing up, going upstairs, going downstairs and other five activities of daily living (ADL) to verify the accuracy of the fall event, the experiment proves the use of BPNN The classification method can successfully realize the occurrence of the identification of fall behavior. The accuracy of the method for offline detection of fall is 100%. In real-time detection, 100% fall sensitivity can also be obtained. All fall behaviors can be detected and the false positive rate is 0.8%. In the process of system operation, there will be no extra brain trouble caused by false positives. This is a satisfactory level of false positives. ADL behavior of different age groups, verify that the system can also be used to make fall judgments in different age groups; after the fall occurs, the fall alarm will be transmitted to the personal computer or mobile device via Ethernet via 4G/Wi-Fi, so that the caregiver can provide the immediate care. Medical care to reduce the damage caused by falls.

誌謝 ii
摘要 iii
Abstract v
表目錄 ix
圖目錄 x
1. 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 文獻探討 3
1.3.1 非穿戴式系統 3
1.3.2 穿載式系統 4
1.3.3演算方法 5
1.4 實驗平台系統架構 7
1.4.1 客戶端 8
1.4.2 使用者(伺服)端 9
1.4.3 監護端 9
1.5 論文架構 9
2. 跌倒事件處理流程及判斷演算法 10
2.1 硬體規格 10
2.2 跌倒判斷演算法流程 12
2.2.1離線訓練階段 14
2.2.2線上即時偵測階段 20
2.3 告警機制方法流程 20
3. 實驗設計與結果探討 22
3.1階段1:訓練測試階段 22
3.2階段2:年齡分群檢測階段 30
3.3階段3:實時檢測階段 36
3.4 階段4:方法比較階段 42
3.5 系統監控方法 45
4. 結論與建議 47
參考文獻 49
自傳 55

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