Detecting Human Behavior Patterns from Mobile Phone

Transcription

1 Journal of Computational Information Systems 8: 6 (2012) Available at Detecting Human Behavior Patterns from Mobile Phone Anqin ZHANG 1,2,, Wenjun YE 2, Yuan PENG 1,2 1 School of Computer Science and Technology, Fudan University, Shanghai , China 2 School of Computer and Information Engineering, Shanghai University of Electric Power, Shanghai , China Abstract Bluetooth integrated into the mobile phone in daily life usage has created new ways to analyze and model the behavior of individuals. In this paper, we use Bluetooth location data from Reality Mining to determine what kinds of repeated behaviors can be detected, determine when a large change occurs in a user s typical routine over time, discovery groups of users that follow certain trends and study the differences and similarities among individuals. Based on the view locations, the human raw movement trajectories can be transformed into binary sequences and human behavior patterns are detected with the proposed BXOR (Binary XOR). Experiments on reality mining cell base data show that the proposed method is effective and reliable. Keywords: Mobile Phone Sensors; View Locations; Xor; Human Behavior Patterns 1 Introduction The mobile phone is a very unique device continuously capturing our location, interaction, communication, and motion traces continuously left behind in our daily lives [1]. Researchers are just beginning to understand the implications of such data collections for fields ranging from epidemiology, dynamical network analysis to human behavior modeling. Human behaviors are complex in nature and it is indeed a challenging task to learn from the daily life activities. Modeling human behavior and individual routines from proximity data and daily life activity patterns is an emerging realm in ubiquitous computing [2]. The recent analysis of data from mobile phone service providers have led researchers to increased insight into human behavior patterns [3]. By far, various works have been done. The work of Eagle et al. [4] on the Reality Mining dataset, marks an early and important contribution. They use principle component analysis (PCA) to visualize temporal patterns in This work is supported by 973 Program (grant No. 2010CB731401), Major Program of NSFC (grant No ), NSFC (grant No ), Ministry of Industry and Information Technology of China (grant No. 2010ZX ), and Science and Technology Commission of Shanghai Municipality (grant No. 09JC ). Corresponding author. address: aqz612@sina.com (Anqin ZHANG) / Copyright 2012 Binary Information Press March 2012

2 2672 A. Zhang et al. /Journal of Computational Information Systems 8: 6 (2012) daily life. The stability of these temporal patterns was confirmed by Farrahi et al. [5] in the same dataset using unsupervised topic models. In [6], periodic behavior patterns were analyzed and used to identify social communities. Algorithms that learn routes between important locations and predict the next location when the user is moving were introduced in [7]. Human mobility patterns have been modeled from location data obtained whenever phone calls were made in [8], to find that human trajectories are highly regular in terms of both temporal and spatial characteristics. In [9], phone call data has been used to study the mean collective behavior of human at large scales, focusing on the occurrence of anomalous events. The authors also investigate patterns of calling activity at the individual level and model the individual calling patterns (time between phone calls) as heavy tailed. Daily activity patterns of individuals are analyzed using large collections of mobile phone data in [10], these patterns reveal that activity patterns within a given area of work strongly resemble each other. In this study we consider one month Bluetooth proximity data of four users taken from reality mining dataset [3] in order to show that the repeated behaviors can be detected using daily Bluetooth proximities. Our goal is to find a person s dominant routines on certain days, determine when a large change occurs in a user s typical routine over time, and discovery groups of users that follow certain trends and study the differences and similarities among individuals and this goal can help us in determining the complex and unusual routines of human behavior. The major contributions of this paper are as follows: (1) We introduce the existing methods related to our discussed problem. (2) We give the idea of the view locations and the method to find the view locations. (3) We define the similarity between two binary sequences and propose an algorithm to detect human behavior patterns. The rest of this paper is organized as follows. In section 2, our approach is proposed. Section 3 presents an experimental evaluation and we conclude in section 4. 2 The Proposed Method In this article, we present a new method to find a person s dominant routines on certain days, determine when a large change occurs in a user s typical routine over time, and discovery groups of users that follow certain trends and study the differences and similarities among individuals. Firstly, find the view locations from the human movement trajectories. Then, for each view location, human s movement data for a certain period of time is transformed from a spatial sequence to a binary sequence according to human s status. Specifically, 1 represents the person stay at the view location and 0 when he does not. Lastly, the proposed BXOR (Binary XOR) algorithm is used on the binary sequence generated by the view locations to detect human behavior patterns. 2.1 Finding the view locations In this section, we will discuss how to find view locations [11]. First of all, we want to show why the idea of view locations is essential to study human behavior.

3 A. Zhang et al. /Journal of Computational Information Systems 8: 6 (2012) Fig. 1: Raw trajectories Fig. 2: Binary sequence viewed from view locations We generate a movement dataset simulating a person s daily activities. Figure. 1 shows his trajectories. From the raw trajectories, we can not find any routine of the person. If we view the data from a certain place, we can find the person s life routine: stay at the certain place or not stay at the certain place. In figure. 2, we transform the movement into a binary sequence, where 1 represents the person is at the certain place and 0 when it goes away. It is easy to see the regularity in this binary sequence. Our idea is to find some place, namely view locations, to view the movement. Furthermore, transforming the raw trajectories into a binary sequence, the spatial noise can be filtered and turn the human daily behaviors detection from a 2-dimensional space (spatial) to a 1-dimensional space (binary sequence). As shown in figure. 2, we do not care where the person goes when he is not at the view locations. Our aim is to find his arriving and leaving pattern. Let D = {(x 1, y 1, t 1 ), (x 2, y 2, t 2 ),...} be the original movement database for a moving person. If we only consider the spatial information of the movement, view locations are frequently visited in the movement. So the view locations are those dense regions containing more points than the other regions. As computing the density for each location in a continuous space is computationally

4 2674 A. Zhang et al. /Journal of Computational Information Systems 8: 6 (2012) Fig. 3: The view locations expensive, we discrete the space into a regular grid and compute the density for each cell. The grid size is determined by the desired resolution to view the spatial data. Algorithm 1: Find the view locations Input: A movement sequence (x1, y1), (x2, y2) (xn, yn) and the grid size l. Output: The view locations. The steps of the algorithm: 1) Discrete the space into a regular grid, there are cells. Each cell has a counter. 2) For each point in the movement sequence (x1, y1), (x2, y2) (xn, yn), judge which cell it lies in, and corresponding cell counter increase 1; 3) Compute the maximum for all cell counters. 4) The points which lie in the maximum cell are the view locations. In figure. 3 the points with the red asterisk are the view locations. 2.2 Similarity measure Identifying the similarity between two sequences is essential for human behavior patterns detection. Computing the average Euclidean distance of two geometric trajectories is a simple and useful approach. Nevertheless, the geometric coordinates are expensive and not always available. Other approaches, such as LCSS, and DTW, are widely used to compute the similarity of symbolic sequences [12]. However, the above approaches suffer from scalability problem. Now in our question, human s movement sequences have been transformed into binary sequences. In the binary sequence, there are only two symbols 1 and 0, Thus, it is convenient to measure the similarity between two binary sequences. If a person stay at the view locations at different period of time, then the corresponding position of the two binary sequences are 1 and if a person not stay at the view locations at different period of time, then the corresponding position of the two binary sequences are 0. For different persons,

5 A. Zhang et al. /Journal of Computational Information Systems 8: 6 (2012) if they stay at the view locations at the same period of time, then the corresponding position of the two binary sequences are 1 and if not stay at the view locations at the same period of time, then the corresponding position of the two binary sequences are 0. So the same symbol 1 or 0 at the same position in the two binary sequences can show the same behavior pattern and the different symbol 1 or 0 at the same position in the two binary sequences can show the different behavior pattern. According to the above fact, we use XOR operation to decide whether it is the same symbol or the different symbol in two binary sequences. Besides, the contrast of the symbol 1 s number and the length gap between two binary sequences can all show the dissimilarity. The difference value of the symbol 1 s number between two binary sequences can be defined as: dif one(b1, B2) = abs(numone(b1)/length(b1) numone(b2)/length(b2)) (1) Where B1 and B2 are two binary sequences, length (B) is a function to count the number of symbol 1 in binary sequence B and length (B) is a function to get the length of the sequence B. The length gap value between two binary sequences is computed via: dif length(b1, B2) = abs(length(b1) length(b2))/min(length(b1), length(b2)) (2) The different symbol 1 or 0 at the same position in the two binary sequences is computed via: dif XOR(B1, B2) = numone(xor(b1, B2))/min(length(B1), length(b2)) (3) So the dissimilarity can be defined as: dissim = difone(b1, B2) + diflength(b1, B2) + difxor(b1, B2) (4) Thus, the similarity can be defined as: sim = 1 dissim (5) 2.3 BXOR (Binary XOR) algorithm Many phenomena show that human often exhibit some degree of regularity in their movements. For a person, he may repeatedly visit some specific places and lives a similar life for different period of time. For different persons, they may have similar life for the same period of time and they can be regarded as similar people to some degree. But it is difficult to mine the person s regularity on the original movements. Viewed from the view locations, the movement sequence can be transformed into a binary sequence B=b1b2...bn, where bi =1 when this person is within the view locations at timestamp i and 0 otherwise. For a person, we can find the view locations using algorithm 1 on his movement sequence, and then transform his movement sequence into a binary sequence viewed from view locations. Thus, we can mine his movement regularity on the binary sequence according to algorithm 2. Algorithm 2: human behavior patterns detection Input: view locations of one person and different persons

6 2676 A. Zhang et al. /Journal of Computational Information Systems 8: 6 (2012) Output: the similarity between different periods of time of one person and between different persons at the same period of time. The steps of the algorithm: (1) According to view locations, the movement sequences are transformed into one person s binary sequences at different period of time and different person s binary sequences at the same period of time. Where 1 represents the person is at the view locations and 0 when it goes away (see figure 2). (2) Compute the difone, the diflength, the difxor via equations (1), (2), (3). (3) Compute the dissimilarity between two binary sequences according to equation (4), and then compute the similarity via equation (5). 3 Experiments and Results In order to ensure repeatability, we carry out our experiments on the publicly available Reality Mining dataset [4]. Although focused on academic mobile phone users, this remarkable resource has served as a basis for a large body of work and is one of the most studied mobile phone datasets. It was recorded over the course of nine months by 97 mobile phone users from the MIT Media Lab and Sloan Business School (students and staff members). Every time a user changed cell tower, the identifier of his new serving cell tower was recorded. We experimented with 4 persons of the 97 individuals in the dataset and days ranging from to This subset of days was chosen randomly. 3.1 One person at different periods of time Fig. 4: No29 person stay at the celltower and the view locations (with red asterisk) from to For person No29, We use the data in the table cellspan from to The table cellspan has recorded start time, end time and the serial number of the person and the serial

7 A. Zhang et al. /Journal of Computational Information Systems 8: 6 (2012) number of the celltower. According to algorithm 1, we can find the view locations in the figure. 4. Fig. 5: NO29 person s movement regularities at 4 periods of time From the figure 5, we can learn directly that the movement regularity of the person 29 at the period of to is some similar with the movement regularity at the period of to 08-29, while the person 29 movement regularity at the period of to is some not similar with the movement regularity at the period of to Using our algorithm 2, we can get the movement regularity similarity between the period of to and the period of to is 0.6 and the similarity between the period of to and the period of to is 0.4. The result shows that the movement regularity at the period of to is more likely similar with the movement regularity at the period of to which tally with the facts. 3.2 Different persons at the same period of time Fig. 6: 4 different persons movement regularities at the same period of time

8 2678 A. Zhang et al. /Journal of Computational Information Systems 8: 6 (2012) In this experiment, we choose 4 persons (No78, No29, No57 and No6) and one week period of time (8-20 to 8-26) randomly. For view locations, here we use home based on the belief that most people will spend much time at home every day. From figure 6, we can learn directly for the same period from 8-20 to 8-26, person No78 and person No29 has the very similar movement regularities, person No29 and person No57 has the dissimilar movement regularities. Using our algorithm 2, we can get the movement regularity similarities at the same period from 8-20 to 8-26, between the person No78 and No29 and between the person No29 and No57 are 0.9 and 0.3. The result shows that the movement regularity similarities got from our algorithm 2 between different persons at the same period from 8-20 to 8-26 is tally with the facts. 4 Conclusion and Future Works In this paper, we give the idea of the view locations and the method to find the view locations and based on the view locations, the human raw movement trajectories can be transformed into a binary sequence and human behavior patterns are detected with the proposed algorithm BXOR. These detected human behavior patterns can help us in determining the abnormal change or an accident in human movement and predicting the future movement. Experiments on Reality Mining Bluetooth proximity data show promising results. In the future, we will develop the methods to detect abnormal change or an accident in human movement and predict the future movement, which obviously needs a great deal of effort and time. There are still other problems such as privacy protection, commercial secrets, and so on need to be considered in our future work. Acknowledgement This work is supported by 973 Program (grant No. 2010CB731401), Major Program of NSFC (grant No ), NSFC (grant No ), Ministry of Industry and Information Technology of China (grant No. 2010ZX ), and Science and Technology Commission of Shanghai Municipality (grant No. 09JC ). References [1] D. Lazer, A. Pentland, L. Adamic, S. Aral, A. L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, and M. Van Alstyne. Computational Social Science, Science, Feb [2] K. Farrahi, Daniel Gatica-Perez, Daily Routine Classification from Mobile Phone Data. IDIAP Research Institute, Martigny, Switzerland. [3] M. Gonzalez, C. Hidalgo, and L.A. Barabasi, Understanding individual human mobility patterns, Nature 453, pp , [4] Eagle, N., (Sandy) Pentland, A.: Reality mining: sensing complex social systems.personal and Ubiquitous Computing 10 (4), pp , 2006.

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