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Concussion balance and postural stability assessment system using kinetic data analysis

University of British Columbia

In current scientific literature, there are numerous approaches that clinicians can use to assess the static postural stability of patients. Among them, the Balance Error Scoring System is a notable method with merits such as cost-effectiveness and portability. Traditional measurement of errors made by patients in BESS test experiment relies on the manual inspection of sophisticated clinicians to the whole experiment process. A new avenue of detecting errors with wireless sensor network and signal processing technique can eliminate the instability from subjective evaluation in traditional method. This thesis present a reliable analytical system that can provide accurate evaluation on errors in BESS test of patient with concussion to assist clinicians to investigate their standing postural stability. In this research, the kinetic signal data is collected by wearable WSN equipment consisting of seven sensors embedded with accelerometer and gyroscope fixed on body of patients while they are completing BESS experiment. We use experimental data of 30 subjects to train back-propagation neural network and test the performance of neural network with testing data set. In this procedure, statistical technique such as principal component analysis and independent component analysis are applied in the step of signal pre-processing. Meanwhile, feature extraction is an alternative pre-processing technique for kinetic signal and the feature data serves as input data to train the neural network. With regard to target training data, the standard error information are acquired from the analysis of a group of researchers on video of the conducted experiment and we present them with Gaussian curve signal indicating the possibility of the error event. By testing the neural network, the technique of feature extraction in combination with back-propagation neural network is confirmed to account for the most optimal assessment of the postural error in BESS test. Furthermore, we can confirm the type of each detected error from six possible types of postural errors with neural network classification technique. Each type of error is corresponding to a certain unstable posture according to “BESS Protocol”. Ultimately, the presented error detecting system is convinced to supply reliable evaluation of the static postural stability of patients with concussion problem.

Text

2014-08-28

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/50256

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/