Accelerometer Only Gait Parameter Extraction With All Convolutional Neural Network
| Autor: | Chen, Chang-Hong, 陳昶宏 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Application of gait parameter extraction blow up in the last few years. For example, medical care, elderly care, sports training, and monitor of daily walking. With the appropriate gait parameter as an index, gait parameter extraction can provide many practical implementations in our daily life and help human. There were some work using the camera system for the research on the gait parameter extraction in the past. The method has the disadvantage of restricting to indoor environmental settings. There are also many double- integration methods using the data from 6-axis sensor. The gait parameter are computed through the double integration and complex algorithm of calibration. In addition to complex calibration procedures, there are also some error accumulations in the computation of gait parameter, which is inferring from the position after double integration. This thesis proposed a CNN model with a single tri-axial accelerometer to do the gait parameter extraction, including stride length, stride height, and the total moving distance. Proposed model is the all-convolutional-layer CNN model for gait parameter extraction. This thesis proposed training method for improving accuracy and reducing complexity, which has greatly reduced the training parameter and the model complexity. The relatively small filter size results in a simpler model with low computational work. Besides, it can be used in both indoor and outdoor environment instead of the complex calibration in the tradition double-integral method. Considering the improving of accuracy, data amount plays an important role in the training of the model. This thesis proposes two data augmentation methods, increasing twenty times data amount for model training. The testing results of proposed model on stride length and stride height with data augmentations both have better result. Considering the reducing of model complexity, proposed method on model simplification has greatly reduced the training parameters and model complexity. Proposed model on gait parameter extraction is done at high sampling rate 500Hz and lower sampling rate 50Hz. Besides, this thesis has successfully done the implementation on android phone and the presenting of the result in real time, which implies the feasibility in practical application. To verify the model on the result of gait parameter extraction, this thesis uses the cross validation. There are total 2154 steps walking data. The proposed work can reach the average percentage error 4.29% with the average error 3.56cm on stride length extraction. With respect to stride height extraction, proposed model can reach the average percentage error 11.65% with the average error 0.63cm. Moreover, the percentage error of total walking distance estimation is 0.1% with 1689 meters. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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