Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/90436
標題: A novel approach for assessing walk speed using waist mounted accelerometers
應用三維加速規於步行速度的估測模型建立
作者: 陳與祥
Yu-Shiang Chen
關鍵字: 逐步迴歸分析
均方根誤差
共線性
Stepwise Regression Analysis
Root Mean Square error
Collinear
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摘要: In this study, we developed a speed estimation model with human body accelerations measured on the waist by a tri-axial accelerometer. To estimate the speed we applied neural network. The sampling frequency is at 200 Hz. A total of 53 subjects (34 males and 19 females, age 22.2±1.32 years, height 170.4±7.7 cm) with the tri-axial accelerometer in their waist and walked 50 meters long hallway in free-living conditions. Each subject's actual average speed were count step by step. We extract eight parameters, namely step number, subject's height, root mean square (RMS) and difference between the maximum and minimum (Range) of vertical , frontal and lateral accelerometer signals. Randomized to 43 subjects for the trained set. We applied stepwise regression to establish the corresponding linear estimation model of walking speed, and used the back-propagation neural network with the same estimation variables to establish walking speed estimation model. Ten subjects for the validation set. The comparison of two models indicates the accuracy of estimation in walking speed. After the statistic analysis, the linear regression equation was obtained by the independent variables (r= 0.93 for trained set, r=0.61 for validation set, RMSE=0.08 m/s for trained set, RMSE=0.242 m/s for validation set). To estimate the walking speed using neural network (r=0.99 for trained set, r=0.98 for validation set, RMSE=0.023 m/s for trained set, RMSE=0.033 m/s for validation set). The relative error for validation set of linear regression and neural network speed estimation is 3.83±1.6%,16.3±0.8%(mean±standard deviation), there is significantly difference (P<0.05). The result shows that using neural networks is more accurate than adopting linear regression analysis in terms of the estimation of a tri-axial accelerometer.
本研究主要以三維加速規應用類神經網路模型來估計步行的平均速度。實驗用的三維加速度規為芬蘭VTI technologies公司所生產的三維加速度晶片(SCA3100-D07),採用取樣頻率200 Hz。利用三軸加速規收集53名受測者(34名男性和19名女性,年齡22.2±1.32歲,身高170.4±7.7公分)行走於50公尺直線走道時的加速度訊號,並紀錄每位受測者實際平均速度,以三個軸向之加速度值方均根、全距值、身高及行走時的步數等八個參數為估測變數,隨機以43名受測者為模型組,應用逐步(stepwise)迴歸分析建立對應的線性步行速度的模型,以相同的估測變數利用類神經網路倒傳遞法建立步行速度的估測模型,10名受測者為驗證組,分別比較兩模型對於估測步行速度的精度。結果顯示線性迴歸所得的無共線性估測變數之步行速度估測模型,對應之模型、驗證組的相關性分別為 r = 0.93、0.61,均方根誤差(RMSE, root mean squared error)分別為0.08、0.242 m/s,類神經網路所得的步行速度估測模型,模型、驗證組的相關性分別為 r = 0.99、0.98,RMSE 分別為0.023、0.033 m/s,兩模型於驗證組的相對誤差分別為 3.83±1.6%、16.3±0.8%,達顯著差異(P < 0.01)。說明了以類神經網路模型用於三維加速規之步行速度估測,顯著優於線性迴歸分析。
URI: http://hdl.handle.net/11455/90436
文章公開時間: 2017-07-07
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