## 4 Discussion  
We classified normal and stroke gait patterns by using data from an IMU sensor placed on the subject's lower back (L5). First, the accelerometer and gyroscope data were segmented based on gait event, which was defined by a 3D-motion-capture system. Then, the time- and frequency-domain features were extracted from the segmented data, and the features which showed statistically significant differences between groups were selected for next-stage feature-selection process. The second stage of the feature-selection process calculated the \(SNR\) values of the features and ranked them accordingly. The results from the second-stage features selection were used to train four SVM kernels, and the trained models were used to test a different test dataset. To the best of our knowledge, this study is the first to classify normal and stroke gait patterns by using features obtained from an IMU sensor placed on the subject's lower back. A previous study from [PERSON] et al., in 2016, used two sensors placed on the shank and waist, and then classified three different gait patterns by using features from the Hidden Markov Model (HMM) [12]. Meanwhile, our study used only one sensor placed on subject's lower back and extracted features from the time- and frequency-domain features of the sensor. Moreover, both time- and frequency-domain features were selected, instead of selecting only time-domain features, because previous studies have shown that frequency-domain features have unique characteristics that can discriminate between normal and pathological gait patterns or activities [8; 12; 33; 35; 36]. A study performed frequency-domain analysis of gait by using an accelerometer, demonstrating the acceleration frequency characteristics of human gait [23; 37]. Feature selection through statistical analysis and \(SNR\) calculation was performed to obtain minimum redundancy, implying that the selected features should be maximally different from each other and have maximum relevance, as well have the strongest correlation with the target variable [29]. The statistical analysis acted as the first stage of feature selection, by indicating which feature has significant differences between groups. Furthermore, the second level of feature selection was done by the \(SNR\) calculation, which ranked the selected features and demonstrated how well the features could discriminate between two classes. One of the selected features was the acceleration RMS in the vertical direction, which was related to gait stability. RMS of acceleration is commonly used in gait analysis study due to its easy calculation and has clear physical value [38]. [PERSON] et al., in 2009, examined the RMS of acceleration data from normal and stroke subjects, while doing level walking with self-preferred speed. The result showed that the raw RMS of acceleration in three different axes was significantly lower in the stroke group, as compared to the elderly group [38]. A previous study using upper-body acceleration investigated gait stability between stroke and normal participants and revealed that the acceleration RMS of stroke participants was slightly smaller than that of the healthy control group [39]. This finding is in agreement with our finding, which indicates that the acceleration RMS in the vertical direction was significantly smaller in the stroke participants than in the healthy participants (young \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Index** & **SVM Linear** & **SVM Gaussian** & **SVM Quadratics** & **SVM Cubic** \\ \hline
**Train/Validation** & & & & \\ _Accuracy_ (\%) & 91.59 & 92.52 & 93.46 & 90.65 \\ _Sensitivity_ (\%) & 92.16 & 90.20 & 90.20 & 88.24 \\ _Specificity_ (\%) & 91.07 & 94.64 & 96.43 & 92.86 \\
**Testing** & & & & \\ _Accuracy_ (\%) & 89.29 & 89.29 & 96.55 & 96.55 \\ _Sensitivity_ (\%) & 82.35 & 82.35 & 94.44 & 94.44 \\ _Specificity_ (\%) & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}