## 3 Results  
### Feature Extraction and Selection  
Forty-two features were extracted from the time- and frequency-domain accelerometer and gyroscope data. Specifically, from time-domain acceleration data (12 features), time-domain angular velocity data (3 features), and frequency-domain acceleration data (27 features). Additionally, among the 42 features, only 15 showed a significant difference between the young-elderly and stroke participants, whereas only one feature showed a significant difference between all groups. Figure 2: Diagram of classification method from data pre-processing, feature extraction and gait classification with Support Vector Machine (SVM) classifier. The 15 features that showed a significant difference between the young-elderly and stroke participants exhibited no significant difference between the young and elderly participants (_p_-value < 0.05). These are outlined as follows: the acceleration standard deviation in the vertical and anteroposterior directions, acceleration skewness in the vertical direction, acceleration RMS in the vertical and anteroposterior directions, first dominant frequency in the anteroposterior direction, power at 3 dB bandwidth in the vertical and anteroposterior directions, lower and upper bound frequency at 3 dB bandwidth in the anteroposterior direction, band power at 99% occupied bandwidth in the anteroposterior and mediolateral directions, bandwidth, and upper bound frequency at 99% occupied bandwidth in the mediolateral direction. The result of frequency characteristic of acceleration on three different axes was calculated by Fast Fourier Transform (FFT), and the result is show in Figure 3. Among these 15 features, the mean value of features from the young and elderly groups was always higher than that of features from the stroke participants. This study classified normal and stroke gait patterns, using data obtained from the IMU sensor placed on the subject's lower back (L5); therefore, we only selected 15 features with significant difference between the young-stroke and elderly-stroke groups. The post hoc test revealed the achievement of statistical power, and the _SNR_ value was calculated for those 15 features; the results are listed in Table 2. The result from the statistical power calculation revealed that all the significantly different features had a statistical power of more than or equal to 0.8. This statistical power analysis indicated that there was lower probability of making Type II error (false negative). In other word, with our current sample size, we had a higher probability of detecting an effect when there was an effect. The second stage of feature extraction was performed for the 15 features. The _SNR_ was calculated for each feature, and the features were ranked according to the calculated _SNR_ values. The four features that had the highest _SNR_ values were selected for SVM training, and their description is explained below:  
* Root mean square of acceleration on vertical direction (RMS ACC V): calculated from root mean square of vertical acceleration data on one stride gait cycle [9; 20; 21; 22]. * Standard deviation of acceleration on vertical direction (SD ACC V): calculated from standard deviation of vertical acceleration data on one stride gait cycle [9; 20; 21; 22]. * Band power on 99% occupied bandwidth on anteroposterior acceleration (Power all AP): calculated from 99% occupied bandwidth of the result from Fast Fourier Transform on anteroposterior acceleration data during whole duration of level walking [8; 23].