dc.description.abstract | In order to make sure the data can be used for subsequent analysis to achieve the purpose of
phenomena discussion and problem analysis, it is important to confirm the validity of the collected data.
This thesis developed a signal preprocessing and screening system to determine the availability of
inertial sensing signals, which often retrieved in the rehabilitation-engineering. This screening system
reduces the impact of bad data by reducing large amount of invalid data. In addition, the system helps
non-professionals to use data and avoid false judgments.
The function of the proposed screening system of this thesis can be divided into three parts:
(1) Data Cleaning and Integration: Confirm the data format and determine the data loss, find the
principal frequency by the spectrum analysis, and integrate the data into a flat file
(2) Data Selection and Conversion: Input motion data can be filtered by 4 common rehabilitation
actions, including walking, shoulder flexion, shoulder abduction and external rotation, which have the
same frequency range. After spectrum analysis, the main three-axis principal frequencies values drawn
in three-dimensional space. Coordinate transformations was used to reduce the dimensions to enhance
the performance of data mining,
(3) Data Mining: The three main IMU frequencies should be the same due to the repetitive and
periodic characteristic of rehabilitation actions. Since slight differences caused by environmental noise,
data tolerance range was established to collect available information.
The function of the proposed screening system was validated by the simulation action data
collected from wireless IMU. This paper also uses the signals of rehabilitation actions measured in the
hospital to confirm the validity in the clinical setting. The result was displayed in a confusion matrix.
The system developed in this study can not only rapidly screen a large number of rehabilitation action
data, but also provide the user to quick judgment on the validity of the input data. | en_US |