臺灣博碩士論文加值系統

本研究將慣性感測器分別放置大腿、小腿、鞋跟後三個不同位置，利用了三種不同的計算方式，包括分別以小腿及鞋後跟的角速度訊號分別計算步態參數，以及運用小腿及大腿的角速度訊號計算步態參數，並將三種方法計算出的步長參數與GAITRite電子步道系統得到的參數做比較。實驗總計30位受測者，在不同空間的條件下，包括上下坡和平地，以兩種步行速度行走，經由統計分析後，發現三種方法計算出步長參數與GAITRite電子步道系統所輸出的步長參數皆有中高度相關性，且經由迴歸分析得到以三種方法預測步態長度的迴歸模型。其中，步道坡度雖具有預測力，但影響因子較小，一次項具有最高的預測影響力，驗證本研究室所開發的足壓感測鞋可以有效估測步態參數。

In this study, three inertial sensors were attached on the thigh, shin and shoe’s back. We used three methods to compute gait parameters, including stride length derived separately from the angular velocity signals of the shin and shoe’s back, and that derived based on the angular velocity signals of the shin and thigh. The derived stride length parameters were separately compared with those obtained by the GAITRite electronic trail system. A total of 30 subjects were enrolled in this study. Each participant walked with two different speeds in three space conditions, including inclining, declining and walking on a flat ground. The statistical analysis shows that the stride lengths derived by three methods were highly correlated with those obtained from the GAITRite electronic trail system. A regression model for predicting stride length was obtained through the second order regression analysis. The slope of walk trail has significant but smaller effect in stride length prediction. The first-order item has the highest predictive influence. This study validates that the effectiveness of gait parameter estimation by foot pressure-sensing shoes developed by our laboratory.

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摘要 iii
Abstract iv
謝辭 v
目錄 vii
圖目錄 ix
表目錄 xi
第一章 緒論 1
1.1 研究背景 2
1.2 研究目的 3
1.3 論文架構 3
第二章 文獻回顧 5
2.1 步態訓練 6
2.2 步態相位 6
2.3可穿戴系統的步態訓練 8
2.4步態參數的偵測 10
2.4.1穿戴式裝置 10
2.4.2陀螺儀與腳壓感測器 12
2.4.3 步態參數的長期測量 14
第三章 實驗裝置與設計 17
3.1 實驗裝置 18
3.1.1 力敏電阻(Force Sensitive Resistor) 18
3.1.2 慣性感測器(Inertial Measurement Unit) 20
3.1.3 GAITRite 23
3.1.4 IMU六軸校正 25
3.1.5 接收介面-Data receiver 28
3.2 實驗設計 30
3.2.1 實驗地點 30
3.2.2 實驗流程 32
第四章 研究分析與結果 33
4.1 資料呈現與整理 34
4.2 資料分析方法 36
4.2.1 狀態機設定 36
4.2.2 閾值(thershold)跟新 37
4.2.3 步態參數計算 38
4.3參數相關性比較 42
4.3.1 空間參數：步幅(stride length) 43
4.3.2 時間參數 45
4.4 空間參數分析結果 46
4.4.1 迴歸分析 46
4.4.2 線性與多元迴歸分析 50
第五章 討論與結論 53
5.1步態參數相關分析 54
5.2 上下坡與平地迴歸分析 54
第六章 未來展望 56
第七章 文獻參考 58


圖目錄
圖 一 加速度FSR訊號及步態週期 7
圖 二 資料傳遞路徑 8
圖 三 步態週期陀螺儀訊號 9
圖 四 IMU和GR參數比較結果 10
圖 五 裝置位置與陀螺儀方向 11
圖 六 步幅計算意識圖 12
圖 七 步幅計算意識圖二 13
圖 八 陀螺儀與FSR訊號比對 13
圖 九 實際空間參數與其估計值 14
圖 十 壓電陀螺儀 15
圖 十一 簡化步態模型 15
圖 十二 足壓及慣性感測模組助行鞋 18
圖 十三 足壓及慣性感測模組 19
圖 十四 助行鞋系統架構圖 19
圖 十五 IMU六軸方向 20
圖 十六 慣性感測器系統架構圖 21
圖 十七 實驗穿戴圖 22
圖 十八 GAITRite資料介面 24
圖 十九 GAITRite 步道裝置 25
圖 二十 加速度計原理示意圖 26
圖 二十一 校正前訊號 27
圖 二十二 校正後訊號 27
圖 二十三加速規頻譜VS 加速規點數(上) 加速規頻譜VS陀螺儀(下) 28
圖 二十四 Data receiver資料解碼格式設定 29
圖 二十五 Data receiver資料接收畫面 29
圖 二十六 慣性感測器和腳壓訊號解碼傳輸方式 30
圖 二十七 斜坡 31
圖 二十八 平地 31
圖 二十九 實驗流程 32
圖 三十 慣性感測裝置六軸訊號圖 34
圖 三十一 肌電與助行鞋六軸訊號圖 35
圖 三十二 助行鞋腳壓訊號 36
圖 三十三 單點狀態機 37
圖 三十四 參數計算狀態機 37
圖 三十五 步幅計算意識圖 40
圖 三十六 swing偵測 41
圖 三十七 時間參數 42
圖 三十八 線性迴歸分析一 47
圖 三十九 線性迴歸分析二 48
圖 四十 線性迴歸分析三 49
圖 四十一 線性迴歸分析四 50

表目錄
表 一 Correlation coefficient and Root mean square difference between inertial- and GAITRite-derived stride lengths during incline walking 43
表 二 Correlation coefficient and Root mean square difference between inertial- and GAITRite-derived stride lengths during decline walking 44
表 三 Correlation coefficient and Root mean square difference between inertial- and GAITRite-derived stride lengths during walking 44
表 四 Correlation coefficient and Root mean square difference between inertial- and GAITRite-derived stride lengths 45
表 五 Correlation coefficient and Root mean square difference between foot pressure- and GAITRite-derived gait parameters during incline walking 45
表 六 Correlation coefficient and Root mean square difference between foot pressure - and GAITRite-derived gait parameters during decline walking 45
表 七 Correlation coefficient and Root mean square difference between foot pressure - and GAITRite-derived gait parameters during walking 46
表 八 Correlation coefficient and Root mean square difference between foot pressure - and GAITRite-derived gait parameters 46
表 九 R2 and β of linear regression 51
表 十 R2 and β of nonlinear regression 52
表 十一 R2 and β of nonlinear regression (roadtype) 52
表 十二 compare roadtype 52
 

Abhayasinghe, N., & Murray, I. (2014). Human gait phase recognition based on thigh movement computed using IMUs. Paper presented at the IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings.
Aminian, K., Najafi, B., Büla, C., Leyvraz, P. F., & Robert, P. (2002). Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. Journal of Biomechanics, 35(5), 689-699. doi:10.1016/S0021-9290(02)00008-8
Bejarano, N. C., Ambrosini, E., Pedrocchi, A., Ferrigno, G., Monticone, M., & Ferrante, S. (2015). A novel adaptive, real-time algorithm to detect gait events from wearable sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(3), 413-422. doi:10.1109/TNSRE.2014.2337914
Casamassima, F., Ferrari, A., Milosevic, B., Ginis, P., Farella, E., & Rocchi, L. (2014). A wearable system for gait training in subjects with Parkinson's disease. Sensors (Switzerland), 14(4), 6229-6246. doi:10.3390/s140406229
González, I., López-Nava, I. H., Fontecha, J., Muñoz-Meléndez, A., Pérez-SanPablo, A. I., & Quiñones-Urióstegui, I. (2016). Comparison between passive vision-based system and a wearable inertial-based system for estimating temporal gait parameters related to the GAITRite electronic walkway. Journal of Biomedical Informatics, 62, 210-223. doi:10.1016/j.jbi.2016.07.009
Grandez, K., Bustamante, P., Solas, G., Gurutzeaga, I., & García-Alonso, A. (2009). Wearable wireless sensor for the gait monitorization of Parkinsonian patients. Paper presented at the 2009 16th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2009.
Lee, S. W., Mase, K., & Kogure, K. (2005). Detection of spatio-temporal gait parameters by using wearable motion sensors. Paper presented at the Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings.
Mazilu, S., Blanke, U., Hardegger, M., Troster, G., Gazit, E., Dorfman, M., & Hausdorff, J. M. (2014). GaitAssist: A wearable assistant for gait training and rehabilitation in Parkinson's disease. Paper presented at the 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014.
Miyazaki, S. (1997). Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope. IEEE Transactions on Biomedical Engineering, 44(8), 753-759. doi:10.1109/10.605434
Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., . . . Bonato, P. (2009). Monitoring motor fluctuations in patients with parkinsons disease using wearable sensors. IEEE Transactions on Information Technology in Biomedicine, 13(6), 864-873. doi:10.1109/TITB.2009.2033471
Rastegari, E., Marmelat, V., Najjar, L., Bastola, D., & Ali, H. H. (2017). Using gait parameters to recognize various stages of Parkinson's disease. Paper presented at the Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017.
Wang, Y., Zhou, H., Wang, Y., Guan, H., Huang, Z., & Li, G. (2017). An acceleration based heuristics algorithm for gait phase detection. Paper presented at the IET Conference Publications.
Yang, C. C., Hsu, Y. L., Shih, K. S., & Lu, J. M. (2011). Real-time gait cycle parameter recognition using a wearable accelerometry system. Sensors, 11(8), 7314-7326. doi:10.3390/s110807314