## 1 Introductions  
Wearable devices, especially ones equipped with accelerometers and inertial measurement unit (IMU) sensors, have been widely used in gait analysis or physical activity monitoring [1, 2, 3, 4]. Wearable devices can be used not only for calculating important gait parameters, such as spatiotemporal parameters [1, 2, 3], or for investigating the gait stability and variability between normal and pathological gait [4, 5, 6, 7], but also for classifying different types of gait patterns, for example, Complex Regional Pain Syndrome (CPRS) gait pattern [5] or Parkinson's disease gait pattern [6]. Wearable devices can open up the possibility of collecting huge amounts of data during daily life conditions. Then, the bigger amount of data collected, combined with specific feature extraction process, will lead to possible application in clinical gait analysis, such as early detection of gait movement disorder. Compared to the conventional method, gait classification with a wearable device would increase the mobility of gait assessment, while still minimizing the subject's discomfort, and it finally would provide the long-term goal of gait assessment in everyday life, in order to be able to do early detection of gait alteration. The gait parameters calculated and analyzed with wearable devices are not only limited to parameters related to clinical-gait performance, such as spatiotemporal parameters (stride time, step time, step length, etc. ), but also features extracted from wearable signals, such as an accelerometer or gyroscope. In fact, a gait spatiotemporal characteristic can also be represented based on features extraction from a set of spatiotemporal local descriptors from gait video sequences [7]. Furthermore, [PERSON] et al. examined gait patterns, using time, frequency, and time-frequency domain features, from an accelerometer signal, which was placed on the lower back of the subject [8]. The statistical analysis revealed that some of the extracted features could differentiate between the healthy and clinical population, which, in that case, included [PERSON]'s disease and Peripherals Neuropathy [8]. Moreover, some studies have used different machine learning techniques to discriminate neurodegenerative diseases, such as [PERSON]'s disease and [PERSON]'s disease [9; 10]. In 2017, [PERSON] et al. proposed a method for successfully distinguishing patients with post-stroke and Parkinson's disease from healthy participants, by using a pair of smart shoes that can measure the ground contact force on both feet [11]. Besides gait pattern, gait stability was also being monitored by wearable devices. In 2012, [PERSON] et al. examined gait stability and harmony in children with Cerebral Palsy (CP) [9]. The gait stability itself can be quantified by measuring the upper-body acceleration's dispersion and smoothness. Meanwhile, the gait harmony is defined as the capacity of our body to transfer the symmetry and rhythmic movement of intra-limb. The gait harmony itself can be calculated by the ratio between the even and odd harmonics from the acceleration signal; it can also be calculated by the ratio between gait parameters from one leg to the opposite leg [9]. Their study found a general reduction of gait stability, which showed by higher root mean square (RMS) acceleration, peak-to-peak angular velocity, and minimal value of acceleration in children with CP [9]. In terms of gait-pattern classification with wearable devices, previous studies from [PERSON] et al. [10] and [PERSON] et al. [11] compared several combinations of IMU sensors placed on the lower limb and trunk. In addition, their studies achieved 89.1% accuracy and 96% accuracy, respectively [10; 11]. However, compared to our study, which used only a single IMU sensor, those studies used several IMU sensors placed on the lower limb and trunks. Furthermore, the other two studies from [PERSON] et al. [12] and [PERSON] et al. [13] utilized less sensors: three IMUs on both shank and lumbar spine, and two shank-mounted accelerometers, respectively.