###### Abstract  
Identifying built environment barriers to walkability is the first step toward monitoring and improving our walking environment. Although conventional approaches (i.e., surveys by experts or pedestrians, walking interviews, etc.) to identify built environment barriers have contributed to improving the walking environment, these approaches may require time and effort. To address the limitations of conventional approaches, wearable sensing technologies and data analysis techniques have recently been adopted in the investigation of the built environment. Among various wearable sensors, an inertial measurement unit (IMU) can continuously capture gait-related data, which can be used to identify built environment barriers to walkability. To propose a more efficient method, the author adopts a cascaded bidirectional and unidirectional long short-term memory (LSTM)-based deep recurrent neural network (DRNN) model for classifying human gait activities (normal and abnormal walking) according to walking environmental conditions (i.e., normal and abnormal conditions). This study uses 101,607 gait data collected from the author's previous study for training and testing a DRNN model. In addition, 31,142 gait data (20 participants) have been newly collected to validate whether the DRNN model is feasible for newly added gait data. The gait activity classification results show that the proposed method can classify normal gaits and abnormal gaits with an accuracy of about 95%. The results also indicate that the proposed method can be used to monitor environmental barriers and improve the walking environment. deep recurrent neural network (DRNN); long short-term memory (LSTM); walkability; wearable sensing; inertial measurement unit (IMU) +
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identifying numerous types of environment barriers, there may be several problems. First, these conventional approaches require time and a budget [14; 15]. Especially, inspection by experts requires excessive financial expenditure when a governmental agency needs to monitor a walking environment in a continuous manner [15]. Second, in the case of self-reporting or interviews with pedestrians, there may be a subjective bias depending on the sampling of pedestrians participating in the survey [17]. Finally, these methods may omit specific environmental barriers since the conventional approaches are performed in an interval manner. For example, some barriers are constant objects (e.g., broken sidewalk) and others are spatial-temporal objects (e.g., a moving bike on a sidewalk). To date, the advancements in sensing technologies can offer an opportunity for overcoming the limitations of the conventional approaches mentioned above. There are two main streams to identifying environment barriers. The first is a vision-based approach. Various methods have been developed to detect potential environment barriers. Generally, vision-based approaches have analyzed images of various environmental conditions [18; 19; 20]. In the analysis process, the approaches have been driven by rule-based criteria such as defective sidewalk regulations [15]. However, the environmental barrier is a relative concept, since each individual has their own characteristics, and there may be various situational contexts in the built environment [21; 22]. Therefore, it is challenging to detect environmental barriers to walkability using one objective criterion [14]. In addition to analyzing images of the walking environment, vision-based approaches can automatedly collect human behaviors that interact with external environmental conditions. Although these approaches have the potential to solve the problems of the conventional methods, the vision-based approaches may go through struggles from the process of collecting scenes that represent interactions between a pedestrian and a walking condition. There are two ways to collect pedestrians' gait images: shooting a walk scene (i.e., closed-circuit television (CCTV) or an experimenter takes photos) and mounting a camera on a pedestrian.