Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories, extending from the longest common sub-sequence (LCSS). Moreover, a DBSCAN cluster validity index (DCVI) is proposed to improve the trajectory clustering performance. The DCVI is used to choose the epsilon parameter for DBSCAN. The proposed method is evaluated using two real trajectory datasets: MIT Badge and sCREEN. The experimental results show that the proposed method effectively detects human trajectory anomalies in indoor spaces. With the MIT Badge dataset, the proposed method achieves 89.03% in terms of F1-score for hypothesized anomalies and above 93% for all synthesized anomalies. In the sCREEN dataset, the proposed method also achieves impressive results in F1-score on synthesized anomalies: 89.92% for rare location visit anomalies (τ = 0.5) and 93.63% for other anomalies.

Location prediction plays an important role in modeling human mobility. Existing studies focused on developing a prediction model which is based solely on the past mobility of only the person of interest (POI), rather than including information on the mobility of her/his companions. In fact, people frequently move in a group, and thus, using mobility data of a person's companions can enhance accuracy when predicting that person's future locations. Motivated by this, we propose a two-phase framework for predicting an individual's future locations that fully benefits from spatio-temporal contexts embedded in that person's and his/her companions' mobility. The framework first determines the POI's companions, then predicts future locations based on mobility information for both the POI and selected companions. Two companion selection methods are proposed in this work. The first method uses spatial closeness (SC) to determine the companions of the POI by measuring the similarity of the individuals' geographic distributions. The second method builds person ID embedding (PIE) vectors, and cosine similarity is used to select the POI's companions. To mitigate the curse of dimensionality, the framework also uses a stacked autoencoder in which the encoder compresses a high-dimensional input feature (e.g., location, time, and person ID) into a low-dimensional latent vector. For the second phase of the framework, a bidirectional recurrent neural network (BRNN)-based multi-output model is proposed to predict a person's future locations in the next several time slots. To train the BRNN model, weighted loss is used, which takes into account the importance of each future time slot to predict the POI's locations accurately. Experiments are conducted on two largescale Wi-Fi trace datasets, demonstrating that the proposed model can effectively predict human future locations.

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