## 4 Discussion

Table 3: Classifier performance assessment result of four different SVM kernel classification. and elderly). Therefore, this feature yielded a satisfactory \(SNR\) value, indicating adequate discrimination between the two classes. Furthermore, the data segmentation into specified gait-event bases, like heel strike or toe-off, was important in our study, because the features that may be useful happened during that specified gait event. The level-walking trial performed by each subject included the phase where the subjects prepared to walk and stop their walk after finishing the trial, thus segmenting the data of the whole duration of the walk into each independent stride (from heel-strike to another heel-strike) was important to make clean data for the classification model. Since SVM relies on hand-crafted features, feeding the classifier model with clean data and highly correlated features is important to produce classifier with high accuracy. Moreover, the duration of each subject's stride or step time was different; thus, segmented data in a constant time frame will create less meaningful features, since the segmentation was created on different gait events. However, there is also a possibility to combine the features segmented based on gait-event with features extracted from whole duration data, such as being done by [PERSON] et al., in 2020, which used whole duration-based features extraction in forms of signal complexity, signal power-spectral-density, and signal magnitude [2]. Trained model validation showed that the SVM classifier with the quadratic kernel had the highest accuracy among the other three models. However, when the trained models were tested by using different testing datasets, both the quadratic and cubic kernels had the highest accuracy (96.55%). Among the 18 stroke datasets, these models misclassified only one subject as a normal subject. This result was anticipated, because the misclassified data came from the subject with mild impairment condition, which may not affect the subject's gait patterns significantly. According to a previous study classifying normal and pathological gait patterns, this result is also expected because this type of classification method focused on gait alteration caused by pathology, not on the pathology itself [12]. Testing model specificity revealed that all SVM kernels exhibited 100% specificity, indicating that no normal subject was misclassified with the stroke gait pattern. This result can be attributed to the mean value derived for the elderly and young participants, which was always greater than the stroke participants, when a significant difference existed between them. Compared with state-of-the-art of the studies, our result achieved the best accuracy, with a testing result of 96.55%. In 2009, [PERSON] et al. classified different gait patterns of post-stroke patients with only a 3D-motion-capture system and not using any wearable devices [40]. Their result showed accuracy of 100% and 86% with kinematics input from the lower limb. However, this study did not use any wearable sensor; thus, it was anticipated that the classification result will be good because it used the golden standard of the 3D-motion-capture system [40]. Furthermore, if we compared with more recent published studies that used a wearable device to classify different gait patterns, we found that our result still outperformed the best result from [PERSON] et al., in 2018, which produced 96% accuracy [11]. However, they achieved their result by using eight IMU sensors attached to the lower limbs and trunk. In addition, another study from [PERSON] et al., in 2016, used only three IMU sensors placed on both the shank and lumbar spine and achieved 90.5% accuracy for classifying healthy elderly, post-stroke, and Huntington's disease groups [12]. The study from [PERSON] et al.