A Quantitative Analysis of Human Calling Behavior During Medical Emergency Calls

With the increasing use of mobile devices, now it is possible to collect different data about the day-to-day activities of personal life of the user. Call Detail Record (CDR) is the available dataset at large-scale, as they are already constantly collected by the mobile operator mostly for billing purpose. By examining this data it is possible to analyze the activities of the people in urban areas and discover the human behavioral patterns of their daily life. These datasets can be used for many applications that vary from urban and transportation planning to predictive analytics of human behavior. In our research work, we have proposed a hierarchical analytical model where this CDR Dataset is used to find facts on the daily life activities of urban users in multiple layers. In our model, only the raw CDR data are used as the input in the initial layer and the outputs from each consecutive layer is used as new input combined with the original CDR data in the next layers to find more detailed facts, e.g., traffic density in different areas in working days and holidays. So, the output in each layer is dependent on the results of the previous layers. This model utilized the CDR Dataset of one month collected from the Dhaka city, which is one of the most densely populated cities of the world. So, our main focus of this research work is to explore the usability of these types of dataset for innovative applications, such as urban planning, traffic monitoring and prediction, in a fashion more appropriate for densely populated areas of developing countries.

Existing predictions of human behaviour trajectories in mobile communication scenarios are mainly based on rules and Markov statistical methods. At the same time, less research has been conducted on CDR data using deep learning methods. This paper proposes the A-TPN (Adaptive Trajectory Prediction Network) algorithm to predict human trajectories for the large scale and sparsity of Call Detail Records (CDR) data. Experiments show that A-TPN achieves a prediction accuracy of 94.76% on real datasets, which is 8.7% better than the traditional Markov statistical computation method. This paper demonstrates the algorithm's effectiveness through rich comparative experiments and analysis.

ABSTRACTIn recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data.

Introduction: In winter 2012-13, severe haze pollution affected major cities in central and eastern China, including Jinan. We investigated whether total medical emergency calls and calls for Central Nervous Systems Syndromes (CNS) increased during this episode and whether emergency calls were associated with particles with aerodynamic diameter ≤10um (PM10) in Jinan, China. Methods: Data on daily medical emergency calls (total and CNS calls) were obtained from Jinan Medical Emergency Center. CNS include dizziness, coma, convulsion, stroke, epilepsy, headache and paralysis. Analysis includes 2 time periods: 1) Dec 1, 2011- Feb 29, 2012 (lower pollution period), and 2) Dec 1, 2012- Feb 28, 2013 (high pollution period). Poisson time series models were used to estimate the change in emergency calls associated with heavy pollution period using an indicator variable, and with day-to-day variation in PM10 levels using a continuous variable. The models were adjusted for temporal trends, temperature, relative humidity and day of the week. Associations were estimated for multiple lag structures. Results: Air pollution levels were high in both the lower and higher air pollution periods with PM10 at 123 ug/m3 and 238 ug/m3, respectively. Compared to lower pollution period, daily number of total calls were significantly higher in the high pollution period (adjusted OR: 1.08; 95% CI: 1.05, 1.11), while the increase of CNS calls was marginally significant. We found positive associations between total calls and PM10 concentrations on lag 0- to lag 5- day. The strongest association was observed for lag 3-day [OR: 1.03; 95% CI: 1.01, 1.05, per IQR increment (121 ug/m3)]. The associations for calls for CNS showed similar patterns. Discussion: Air pollution levels are alarmingly high in winter in Jinan. Results indicate that substantial health benefits would result from efforts to prevent future severe episodes of haze pollution.

Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes.

With fresh development in the area of artificial intelligence and machine learning, the analysis of human physiological and psychological behavior has received greater attention around the world. In this paper, we have provided a detailed survey of the approaches used for human behavior detection considering different modalities with physiological behavior, psychological behavior, and emotion detection with the help of sensors EEG, ECG, GSR, and temperature. At long last, it finishes up with the results of this study and represents the thoughts for future exploration in the zone of human behavior understanding. A rundown and comparison among the ongoing investigations is done that uncovers the currently existing issues. In addition, future work has been examined.

Machine learning techniques have been used to predict customer behavior in telecommunications industry. Typically, several data sources, including historical usage, billing, payment, network, customer service, and demographic data, can be used in a predictive model. However, in some cases, e.g., in the prepaid customer segment, there is often little data available except for the call detail record (CDR) data. In this paper, we tackle this challenging problem, using significantly delayed CDR data as the primary data source to predict customer behavior. We extract calling links, i.e., who called whom, from the CDR data, and propose several distance measures based on calling links. We demonstrate that, by using information derived from the calling links alone as inputs to a neural network model, an acceptable accuracy for predicting churn (customer switching from one service provider to another) can be achieved. Calling links can also be used to identify calling communities, which may be used for targeted marketing campaigns and help predict acceptance of marketing offers.

Examining potentials and practical constraints of mobile phone data for improving transport planning in developing countries

The interior of urban rail transit system has always been the blind area of traditional passenger flow monitoring methods. The call detail record (CDR) data is a kind of data containing passenger travel information with the advantages of wide coverage and no dead angle. It can well complement the shortcoming that traditional passenger flow monitoring method has blind areas. This paper proposes an approach of urban rail passenger flow monitoring based on CDR data which can directly monitor passenger flow in metro station and passenger flow on different routes. It makes up the defect that previous research can only estimate passenger flow by models. Experiment with actual data, the one-week passenger flow of Beijing Metro Line 2 is monitored. It shows that the characteristics of passenger flow are closely related to the nature of land use and passengers are more likely to be affected by transfer times when transferring. The experimental results match reality and verify the feasibility of the algorithm.

This paper is concerned with characteristics of human behaviors in Internet-based teleoperation system under communication delay. Especially frequency character and velocity character of human behaviors affected with random delay of the Internet are analyzed. We are researching in human adaptive network mechatronics which can not only evaluate operational behaviors of human, but also help the operator's control according to the evaluation of operations in Internet-based teleoperation system. The characteristics analysis of human behaviors which are affected by delay of the Internet will provide us helpful regulations to design control methods in human adaptive network mechatronics. We develop an experimental system which includes a joystick controller and a virtual inversed pendulum. The joystick can control the virtual pendulum with the Internet delay which has been recorded in a test before. The Laminar Phase analysis and power spectrum is utilized to analyze human behaviors affects by delay of the Internet.

A model of human behavior in emergency situations in confined spaces with a complex structure. A distinctive feature of this model is that the information influence of the behavior of agents in the group is individual. The level of exposure to the influence of the information agent depends on the type, based on some factors, such as location relative to the group members, the physical and functional characteristics of the individual membership. By the functional affiliation is meant the role of agent upon condition the absence of some external destructive influences. The proposed model describes four possible physical states of the agent: alive and active; alive and passive, wounded; received critical damage, unable to move; dead. The developed theoretical foundations models have been implemented in a software environment simulation by the example of incident in the club “Lame Horse” located in Perm, Russia, 05.12.2009. The experiments showed results corresponds to the actual outcome of the incident model operating results with considering the probability of deviations.

Medical emergencies in the imaging department of a university hospital: Event and imaging characteristics

A hierarchical neuro-fuzzy architecture for human behavior analysis

Controversies in the evolutionary social sciences: a guide for the perplexed

Owing to the complexity of human behaviors in risk situation and the harsh physical environment of underground mine, current researches on coalmine virtual environment have paid little attention on modeling human risk behaviors. Based on Artificial Life method, an intelligent virtual miner agent model was proposed to yield better understandings of human factors in emergency situation and to assist risk behaviors analysis in underground coalmine. The virtual miner agent is a comprehensive model of 3D virtual human and combines perceptual, behavioral, internal states and cognitive modules. And then, behavioral model of virtual miner was introduced in detail. Behaviors of virtual miner are constructed by a set of parameterized basic actions and are controlled by personalized action selection mechanism, which incorporate miners' cognitive processes and internal state characteristics to explore human behavioral mechanism of risk accidents. The preliminary results of application case show the effectiveness of our method to simulate human risk behaviors in coalmine virtual environment.