###### Abstract
The first method can require lots of CCTV or experimenters that may be costly and labor-intensive. In addition, the first method cannot capture an exact gait scene when an interaction between a pedestrian and a walking condition occurs. The second approach is to utilize wearable sensors. Considering human behaviors during walking are affected by external conditions, abnormal external conditions that can be understood as an environmental barrier may cause abnormal behaviors in pedestrians [23]. The previous studies have already proven that there is a change in human behavior or response in the presence of external stimuli. For example, [PERSON] et al. [15] investigated the relationship between abnormal behaviors and defective sidewalks by using pedestrians' walking data collected from an inertial measurement unit (IMU). [PERSON] et al. [24] investigated the relationship between environmental barriers and physiological data such as the average of the Maximum Lyapunov Exponential (MaxLE) and heart rate values. [PERSON] et al. [25] used various wearable sensors (i.e., EDA, gait patterns calculated as MaxLE, and blood volume pulse) to evaluate the neighborhood built environment. [PERSON] [26] measured walkability by combining biosensors and geospatial analysis. Although the above-mentioned studies presented the potential uses of wearable sensors for detecting environment barriers by using physiological data, they mainly focused on the magnitude of signals. Human gaits are very diverse under the conditions of the walking environment. Human gait patterns are usually constant when there is no environment barrier. On the other side, the gait patterns on environment barriers may become abnormal [14; 15; 16]. Therefore, if we can differentiate between abnormal gaits and normal gaits, we can find the environmental barriers that cause abnormal gaits. Existing wearable-based methods mainly used the intensity of the response to differentiate between abnormal gaits and normal gaits [27]. Although the existing method has shown sufficient potential to detect the hazard, the intensity-based approach to the reaction does not sufficiently consider the various types of human gait. For example, some people may be very wary of environmental barriers (weakening the intensity of their responses), while others may be more sensitive to environmental barriers [28]. A method that can distinguish abnormal gaits from normal gaits, including these personal characteristics, is needed. The recent development of wearable sensor-based human activity recognition (HAR) can provide an opportunity to address the current limitations of environmental barrier identification. The HAR technology has generally been formulated [29]. As shown in Figure 1, the general data-processing flow for HAR consists of (1) collecting the activity-related data from wearable sensors during performing activities, (2) extracting relevant features that can describe the sensed information, (3) using a learning method using labeled data, and (4) applying the learning method to new unknown data for HAR [29; 30]. In the early stages of HAR research, previous studies mainly used two approaches to extract features from time-series data: statistical and structural [31]. These approaches are hand-crafted methods that transform the raw data into particular pre-defined descriptors [32]. Hand-made features and shallow learning algorithms have been used to classify activities [33]. A shallow learning algorithm can be defined by the low depth of the paths of trainable units between the input and output layers [34]. When the low depth of a shallow learning algorithm becomes deeper, the algorithm turns to deep learning architecture. Recently, there have been lots of deep learning approaches for wearable sensor-based HAR [35; 36; 37; 38; 39]. In the early stages of applying deep learning methods, deep belief networks (DBNs) were used [40]. DBNs were built by staking multiple layers of a restricted Boltzmann machine (RBM).