Performance-Oriented and Hardware-Oriented Freezing of Gait Detection Algorithms Using Time-Frequency Analysis
Date Issued
2014
Date
2014
Author(s)
Hu, Hao
Abstract
Parkinson’s disease (PD) patients have difficulty in walking. They usually suffer from the Freezing of gait (FOG) problem, which interferes PD patients’ life.
We develop two algorithms which can detect FOG of PD patients. The first algorithm is performance-oriented. In this part, we develop an algorithm which uses fast time-frequency analysis methods and onset detection to detect FOG in real time. Simulation results show that the specificity can achieve 81.83%, and the sensitivity and accuracy are 82.66% and 82.83%, respectively. The sensitivity and the accuracy are higher than those of other algorithms. Our algorithm can help PD patients overcome the difficulty of walking and help them live better if the algorithm can be realized on a healthcare devise. More importantly, the time delay of the proposed algorithm is only 0.95 second, which is less than that of other algorithms.
The second algorithm is hardware-oriented. In this part, we use the integer transform and replace the smooth filter by the IIR filter to reduce complexity. We use a recursive formula of Fourier Transform to implement the output instead of Fourier transform in order to decrease the complexity. Moreover, the original asymmetric windows are replaced by recursive asymmetric rectangular window, triangular window, and polynomial window, which is zero order, first order, and forth order polynomial formula of recursive form, respectively. The more the order is, the better the performance will be, meanwhile, the complexity will be higher. The fast method with forth order polynomial form with asymmetric polynomial window is able to detect the FOG with sensitivity of 77.47%, specificity of 77.61%, and accuracy of 78.02%. The average amount of multiples of each point of the proposed hardware-oriented FOG algorithm with rectangular asymmetric window needs 11. On the other hand, the average number of multiples of each point for the original performance-oriented method is 19.16. Therefore, compare to the performance-oriented FOG detection algorithm, the hardware-oriented FOG detection algorithm is even more suitable for implementing on a hardware-device because of its lower complexity and competitive performance.
Subjects
時頻分析
整數轉換
起始偵測
帕金森氏症
凝態步伐
Type
thesis
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