## 1 Introduction  
Walking is one of the most common forms of physical activity that promotes individual mental and physical well-being. There are many health, environmental and economic benefits associated with high walking levels in communities [1; 2; 3; 4], which has led to increased interest in maintaining walkable neighborhoods through walkability assessments. Walkability can be defined as the way individuals perceive the quality of the walking environment by measuring the friendliness of the built environment to walking [1; 4]. Although many built environment features are used to define walkability, the presence and quality of sidewalks serve as substantial indicators influencing the perceived safety and overall satisfaction within the pedestrian environment [5; 6]. Therefore, sidewalk assessments are an essential component of walkability assessment tools [7]. Pedestrian interviews or surveys are examples of commonly practiced sidewalk assessment methods that consider pedestrians' perceptions [8]. Nevertheless, these responses can be biased and lack expert insight. Government agencies also rely on trained experts to perform on-site inspections to identify violations of pre-defined regulations [9]. These conventional practices are ineffective and inefficient because they require significant financial expenditure, time and intensive labor. Hitherto, the limitations mentioned above can be addressed with the advancement of sensing technology. The methods for automated sidewalk assessments can be divided into two categories: approaches based on infrastructure-based data collected from the surroundings and approaches based on wearable sensor data. For approaches based on infrastructure-based data, various advanced methods have been presented for applying deep learning techniques on infrastructure-based data to automate sidewalk assessments. Some examples of these urban data include street-view images, videos or geographic information system (GIS) data. Several works have also explored using deep learning approaches on vehicle responses measured with smartphone inertial sensors mounted on vehicles to identify sidewalk and roadway anomalies. However, these approaches do not consider the involvement of pedestrians in their evaluations. On the other hand, utilizing wearable sensors for automated sidewalk assessments considers individuals' behaviors. Wearable sensors can be placed on pedestrians to measure human physiological reactions and the signals captured can in turn be used to examine how human physiological reactions are influenced as a result of their surrounding environments. Earlier studies demonstrated that various sidewalk features or defects led to changes in human response. Yet, these studies focused on investigating the association of signal magnitude or specific gait parameters with surface conditions or built environments with statistical modeling. Thus far, there have been limited studies involving pedestrians in sidewalk surface condition assessments using machine learning or deep learning techniques. Nevertheless, existing works were either not generalizable to individual differences due to the absence of gait features, thereby lacking the ability to capture biomechanical characteristics, or the focus was not on assessing sidewalk surface conditions for the purpose of a walkability assessment. To the best of our knowledge, none of the existing works explored incorporating gait analysis techniques with machine learning or deep learning approaches for sidewalk surface assessments. To bridge this gap, in our previous study [10], we identified the ideal body location for placing a sensor, which is at the right ankle. Then, we developed a traditional machine-learning-based classification framework for identifying irregular walking surfaces using gait features extracted from the right-ankle sensor to train machine learning models and demonstrated the effectiveness of the approach [10].