## 2 Related Works
### Deep Learning Methods for Automated Sidewalk Assessments  
Numerous advanced deep-learning-based approaches have been developed for the automated evaluation of roadways or sidewalks utilizing infrastructure-based data such as images, videos or GIS technologies [26; 27; 28; 29; 30; 31; 32]. In one study [33], acceleration data from smartphones mounted on vehicles were used to train deep learning models for road surface monitoring and pothole detection. However, determining the presence of poor sidewalk conditions relies on how individuals interact with sidewalks [34], and such interactions can vary in different contexts [35]. Infrastructure-based data and data collected from moving vehicles, nonetheless, fail to address how the human body reacts to external walking environments [36]. Individuals respond differently to the same surroundings based on their characteristics; therefore, their variability must be analyzed [8].  
Limited studies have applied deep neural networks to analyze human responses for walking surface condition detection. [PERSON] et al. verified the effectiveness of a cascaded LSTM-based deep recurrent neural network method to classify abnormal and normal gaits, and demonstrated that the ratio of abnormal gaits could indicate the existence of an environmental barrier to walkability, as they were highly correlated [37]. The confirmed relationship between the ratio of abnormal gaits and the presence of an environmental barrier is in line with our observation of a higher rate of disrupted gaits on irregular walking surface segments. However, the focus of their study on the environmental barrier was on surrounding facilities instead of sidewalk conditions. [PERSON] et al. demonstrated the effectiveness of using deep learning approaches to analyze data from six wearable sensors placed on different body locations to identify irregular walking surfaces [38]. However, the surfaces that were tested in the study did not accurately reflect the usual uneven surfaces found on sidewalks in neighborhoods. Furthermore, deploying sensors at various locations for continuous monitoring in real-time is impractical due to the computational demands of processing data from multiple locations [39], as well as the discomfort caused by wearing multiple sensors. [PERSON] et al. also expanded their study to propose a deep learning approach by training a convolutional neural network (CNN) with acceleration data and window-based features extracted from smartphones [40]. The limitation of this approach is that the models trained with features extracted from smartphone data using a sliding window were unable to be generalized to individual differences.  
To address the gaps in existing works and to improve the prediction performance of the traditional machine-learning-based classification framework from our previous study [10], we introduced a novel deep-LSTM-based classification framework to automatically detect irregular walking surfaces with a single wearable sensor in this paper. In our previous study [10], we identified the right ankle as the ideal location for sensor placement for detecting irregular walking surfaces. Hence, in this paper, we used the acceleration data collected from a right-ankle sensor from our previous experiment and the traditional machine learning approach as the benchmark to validate the feasibility of our proposed deep LSTM framework.