###### Abstract
Afterwards, DBN-based models implemented a hidden Markov model (HMM) on top of the RBM layer to exploit the unique temporal sequences of human activity [41]. However, HMMs are limited by the number of possible hidden states and are impractical when modeling long-range dependencies in large context windows. [PERSON] et al. [42] proposed convolutional neural networks (CNNs) for HAR. Although the suggested model presented the possibility of the CNN model for HAR [42], they used a shallow model, not a deep learning model. CNN models using a deep learning model can classify various human activities because CNN models can learn robust and identifiable features and capture local dependencies between nearby input samples by utilizing convolution over one-dimensional time sequences [29; 43]. CNNs use time-dependent parameter sharing and local connections between adjacent layers to capture local dependencies [44]. However, sharing parameters over time is insufficient to capture all correlations between input samples. Local connectivity limits the output in a function of a small number of neighboring input samples [45; 46; 47]. A DRNN model was proposed to utilize the internal memory to capture the temporal dynamics of the activity sequence. [PERSON] et al. [48] optimized DRNN to achieve high throughput and short recognition time. In particular, the authors proved that the DRNN model is a suitable method for HAR by comparing the time taken by the existing shallow learning models such as support vector machine (SVM) and decision tree with the time taken by the optimized DRNN model. In addition, when using only DRNN without using CNN in HAR, DRNN has the advantage of responding to window sizes of various lengths instead of the fixed-length window size required by CNN. Considering that the length of the gait pattern (i.e., stride length) varies from individual to individual, it is judged that the  
Figure 1: General data-processing flow for HAR. DRNN model can distinguish between normal gaits and abnormal gaits. A representative study that showed this possibility is the study of [PERSON] and [PERSON] [45]. This study proposed a unidirectional, bidirectional, and cascaded long short-term memory (LSTM) architecture based on data collected from wearable sensors. All three methods showed excellent HAR performance, but the author decided to apply the cascaded LSTM-DRNN model in this study. The reason for using this model is that [PERSON] and [PERSON] [45] showed a higher F1 score compared to KNN or CNN in the Daphent Freezing of Gait Data Set (Daphnet Fog), which is data for classifying gait patterns in people with Parkinson's disease. This study aims to identify the environmental barrier through the classification of normal gaits and abnormal gaits caused by the environmental barrier using wearable sensor data. To this end, the author labels and applies the experimental data performed in the author's previous study [27] to the cascaded LSTM-based DRNN model [45; 49] for training and testing the model. Afterward, the feasibility of the model for new data is checked by applying the data of the experiments conducted at the same site to the built model. The remaining parts of this paper are organized as follows: Section 2 introduces the methodology, including how the wearable sensor-based gait data collects, how the cascaded LSTM-based DRNN model is composed, and how the model is trained and tested. Section 3 illustrates the results of (1) testing and training the model and (2) investigating the relationship between the presence of an environmental barrier and HAR results (normal/abnormal gaits). Several discussion points are presented in Section 4. Section 5 wraps up this study and suggests future research.