| DC 欄位 |
值 |
語言 |
| DC.contributor | 機械工程學系 | zh_TW |
| DC.creator | 林益民 | zh_TW |
| DC.creator | Yi-Min Lin | en_US |
| dc.date.accessioned | 2013-8-16T07:39:07Z | |
| dc.date.available | 2013-8-16T07:39:07Z | |
| dc.date.issued | 2013 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=100323069 | |
| dc.contributor.department | 機械工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 本研究主旨為利用貼附於手腕及手肘關節處的MARG感測器，擷取上肢於運動過程的慣性訊號，作為所發展動作軌跡重建演算法的輸入訊號，並分別驗證軌跡重建結果之正確性。研究中利用動作中所擷取的角速度、線加速度、及磁場訊號，經無跡卡曼濾波器演算法融合九軸訊號後，得到以四元數法呈現的姿態，可避免以尤拉角表示所產生的奇異點問題，並經由所規畫的上肢動作模式，還原動作軌跡。然而，在上肢動作軌跡重建研究中，棘手處在於利用重力加速度及地磁訊號所還原的姿態，將受動作加速度以及周遭環境磁場的干擾，產生嚴重誤差。為解決上述問題，相較於以試誤法獲得卡曼濾波器參數，本研究將動作加速度視為低通濾波後的雜訊，並基於環境中僅有地球磁場，進行補償動作加速度、以及周遭環境磁場干擾所造成的誤差；在此情況，只需改變一個與動作加速度相關的參數，即可得到最佳的姿態解，如此容易實現且結果也更精確。
在演算法驗證部分，比較還原後的上肢動作軌跡和事先設計好的軌跡，並計算兩者間各軸向的均方根誤差(皆小於5.2 cm)、以及所觀察平面上的相關性係數(大於0.92)，即可客觀分析軌跡重建的成效。 | zh_TW |
| dc.description.abstract | This study aims to develop motion trajectory reconstruction algorithms, using captured inertial measurement data from wearable inertial and magnetic sensors mounted near the joints. In this study, we use unscented Kalman filter to derive a quaternion representation of orientation, which describes the coupled nature of orientations in 3-D and is not subject to the problematic singularities associated with an Euler angle representation from an optimal fusion of 9-axis signals, including 3-axis angular rate, 3-axis acceleration, 3-axis magnetic field signals. Then, we can reconstruct movement trajectory by using the proposed upper limb motion model with derived quaternion representation of orientation. In this research, we consider motion acceleration as low-pass filtered noise for compensating disturbance caused by motion. One of the significant contributions of this research is the use of this only one motion acceleration parameter which is modified by cut-off frequency. It can be achieved easily and the reconstructed results are more accurate in comparison to Kalman filter, which learns by trial and error and takes a lot of time.
As the verification of developed algorithms, we evaluate the reconstructed trajectory by computing the RMS errors of joint position and correlation coefficients between ideal trajectory and the reconstructed trajectory. | en_US |
| DC.subject | MARG感測器 | zh_TW |
| DC.subject | 軌跡重建 | zh_TW |
| DC.subject | 無跡卡曼濾波器 | zh_TW |
| DC.subject | Inertial and magnetic sensors | en_US |
| DC.subject | Trajectory reconstruction | en_US |
| DC.subject | Unscented Kalman filter | en_US |
| DC.title | 基於慣性及磁場感測訊號之上肢運動軌跡重建研究 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |