wandering detection and activity recognition for dementia...

115Wandering Detection and Activity Recognition for Dementia Patients Using Wireless Sensor Networks

Wandering Detection and Activity Recognition for Dementia Patients Using Wireless Sensor Networks

Sang-Ho Na, Kyu-Jin Kim, Eui-Nam HuhDepartment of Computer Engineering, Kyung Hee University, Global Campus, Korea

[email protected], [email protected], [email protected]

Abstract

A central goal of information technology is to improve human life. In terms of useful technology in the area of sensor networks, activity recognition (AR) has become a key feature. Using AR technology, it is now possible to understand human behavior, including what, how and when people perform an activity. In recent years, there has been an increase in accident reports involving aged dementia patients, resulting in higher social costs to treat and care for dementia patients. AR technology can be utilized to take monitor the activity of these patients. In this paper, we present an efficient method that converts raw sensor data to readable patterns in order to classify an individual’s current activities and then compare these patterns with previously stored patterns to detect any abnormal patterns, such as wandering, which is one of the early symptoms of dementia. We used this method to digitize human activities and applied the Levy-walk model to detect wandering patterns. We developed an inference model for an early dementia symptom based on digitized human activity patterns. In this article, we also illustrate the implementation of a sensor system configured with tri-axis acceleration and an ultrasonic sensor as well as a wandering estimation algorithm in order to overcome limitations of existing models to detect/infer dementia.

Keywords: Activity recognition, Dementia symptom, Detecting wandering, Mobility, Levy walk.

1 Introduction

Industrialization has resulted in population aging. The number of dementia cases is increasing as fast as the aging population grows. Dementia is a brain-disease-syndrome that occurs primarily in older people. In a broad sense, the brain dysfunction has a distinct relation to cognitive disability [1]. The symptoms of dementia may include repetition of simple actions such as wandering outside and collecting objects, asking the same questions repeatedly, becoming lost in familiar places; being unable to talk, read or write, etc. Many research centers are focusing on the prevention of various diseases in older people. Government policies and technologies associated with these problems

are developing rapidly. Caring for dementia patients involves special social facilities and human resources, as well as financial, physical and emotional support from families.

Dementia is regarded as one of the major problems for older people and hence effective care must be provided by the families as well as society. A long-term-care-insurance-law for dementia patients was started in South Korea, but it has limited coverage for eligible individuals. With a more detailed knowledge of dementia symptoms in the early stage, better care can be provided for specific symptoms, thus significantly reducing the cost for dementia treatment. It is known that the early diagnosis of dementia symptoms is difficult. But with recent advances in medical research, dementia symptoms can be uncovered through cognitive disability tests and psychological analysis. Moreover, psychological analysis can be used to determine whether the dementia is temporal or permanent. However, the tests for medical diagnosis of dementia are performed in hospitals because of the cost and time it would take to detect symptoms in the home. Hence, an easy and cost effective approach to diagnosis and treatment of dementia patients is needed to reduce the economic burden on families and society and to resolve difficulties related to the complex medical diagnosis for dementia.

As mentioned above, one symptom of dementia is the repetition of a simple action such as wandering outside. This can be detected by the activity recognition (AR) system. Activity recognition in an older person’s daily life should be done indirectly and in a comfortable manner. Complex equipment such as markers and camera has been widely used for activity recognition, but this approach is disruptive to a person’s daily activities.

The major intellectual contributions of this work include the following:

First, we propose efficient activity recognition and location detection method using tri-axis acceleration and ultrasonic sensors to digitize traceable behavior of an individual during daily activities. We also illustrate a modeling method for observed behavior data and a detection method for dementia pre-symptoms. Second, we apply our algorithms to early dementia patients at the “Eden Senior Sanatorium Center (Eden Center).”

The rest of the paper is organized as follows: Section 2 explains symptoms of dementia, and describes the existing

*Corresponding author: Eui-Nam Huh; E-mail: [email protected]

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Journal of Internet Technology Volume 13 (2012) No.1116

activity recognition system, location tracking system and human mobility model. Section 3 shows the proposed activities determination system and an algorithm for activity recognition and wandering detection using the Levy-walk model. We also describe the experimental results with our system. Finally, a conclusion and future research direction are discussed in Section 4.

2 Related Works

2.1 Symptoms of DementiaDementia is a syndrome of brain disease in elderly

people that includes cognitive functional disorders of the brain [1-2]. Cognitive functions include cerebral functions such as linguistics, memory, attention and movement. DSM-IV (American Psychiatric Association, 1994) states that dementia can be verified by disorders involving more than two cognitive functions and memory failure [3]. In the past, the diagnosis of dementia was very difficult, but it has been made easier by the introduction of cognitive examinations.

One of the symptoms of dementia is a change in sleep characteristics [9]. A decrease in the amount of sleep is the most remarkable feature of early dementia. Patients with early dementia wake up in the middle of sleep and in the early morning. In addition, due to a failing of memory, wandering to find a room or bathroom frequently occurs [1].

Figure 1 shows the sleeping pattern of an early dementia patient at the “Eden Senior Sanatorium Center” in Gyeonggi-do, Korea. The lower line represents a sleeping phase and the higher line is an active phase. As we can see from Figure 1, several active phases occur at dawn. These attributes can be used to predict and diagnose dementia.

Figure 1 Sleeping Pattern

2.2 Activity Recognition 2.2.1 Low-Level Activity

Low-level activity is a physical movement such as walking, running or lying. 1-axis acceleration sensors and ultrasonic sensors were used in a previous study of low-

level activity recognition. These sensors can determine if a subject is moving or not. These sensors have limited function and provide little information of activity data. Many algorithms utilize tri-axis acceleration sensors for activity recognition [4-7][19-20].

Activity recognition using tri-axis acceleration sensors is estimated based on an x-, y- and z-axis graph produced with one or many tri-axis acceleration sensors. The shape, variation and value of that graph can be used to classify static and dynamic behaviors. Static behaviors include lying, standing and so on, while dynamic behaviors include walking and running, etc.

Low-level activity recognition is based on data collected by sensors. If the subject moves, we can analyze the sensor’s data packets to determine the duration of the movement. But there are many high-level activities that cannot be determined based on the analysis of the packets. 2.2.2 High-Level Activity

Studying, eating, watching TV, sleeping (not lying), etc. are examples of high-level activities. High-level activities are predicted using time and place. For example, if a person is in the bedroom from 11:00 am to 6:00 am and we do not observe any movement, then we can predict that he/she is in a “sleeping” state [8].

However, high-level activity recognition may have limitations. The information can be wrong sometimes because it uses data based on the sensor combined with the researcher’s experiences and predictions. So it is difficult to recognize high-level activities using only the sensor’s raw data. To get the best results with high-level activity recognition in daily living, sensors need to be attached to every joint of the body, which is impossible. Therefore, an efficient algorithm is required that can extract the maximum information using the minimum number of sensors to get the high-level activity information. There has been minimal research regarding investigation of human activity recognition. In next subsection, we will examine human activity methods using accelerometer sensors.2.2.3 Accelerometer Signal-Based Human Activity

RecognitionAccelerometers have proven to be a practical,

inexpensive, and reliable choice for monitoring motions and postures under free-living conditions [12-13]. In [12], the authors assessed the feasibility of using a waist-mounted, wireless triaxial accelerometer (TriA) to monitor human movements in an unsupervised home setting to detect changes in functional status. They did a pilot study with six healthy subjects aged 80-86 years. The subjects wore a TriA unit every day for two to three months. The TriA system was found to be practical for long-term, unsupervised home monitoring. All subjects found the system simple to use and the TA unit was unobtrusive and comfortable to wear.

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In [13], the authors presented an implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is the performance of a vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure.

The authors in [13] proposed a human activity recognition technique using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The technique helps to categorize human body postures such as sitting and standing and locomotion such as walking and running. The proposed framework is general such that it could be extended for use with several accelerometers. The authors combined features such as signal magnitude areas (SMA) and title angel (TA), which have been used separately to distinguish dynamic and static activities, into one system such that dynamic and static activities could be recognized via the same system.

2.3 Location TrackingOne of the remarkable attributes of early dementia

patients is wandering in the middle of sleep time. Wandering can be difficult and hard on family members involved in the patient’s daily life. Therefore, activity recognition as well as location tracking of dementia patients is crucial for the silver-care system.

There have been studies devoted to mechanisms for location estimation for silver-care using Zigbee and ultrasonic sensors [10-11]. Ultrasonic sensors use the time between the transmission of an ultrasonic sensor device to the sensor detecting devices to calculate distance, thus determining the location and positioning of the designated sensor device. Therefore, the synchronization of time between the transmitting and receiving devices is vital in calculating and providing the exact distance and positioning of the concerned object. The distance between the transmitting and receiving devices is calculated using the method shown in Figure 2.

2.4 Human MobilityAs wireless devices are often attached to humans,

understanding their mobility patterns leads to more realistic simulation. Commonly used mobility models include random way point (RWP) or random walk models such as Brownian motion and Markovian mobility. These models are simple enough for theoretical analysis and experimental

simulation. However, there has been little statistical validation of such models for accuracy in describing human mobility.

Recently, researchers have reported that human walking patterns in an outdoor setting over tens of kilometers resembles a truncated form of Levy-walks, which are commonly observed in animals such as monkeys, birds and jackals [14-16]. Their studies were based on about one thousand hours of GPS traces involving 44 volunteers in various outdoor settings including two different college campuses, a metropolitan area, a theme park and a state fair. They show that many statistical features of human walking patterns follow truncated power-law, showing evidence of scale-freedom, and do not conform to the central limit theorem. These traits are similar to those of Levy-walks. It is conjectured that this truncation, which makes the mobility deviate from pure Levy-walks, comes from geographical constraints including walk boundary, physical obstructions and traffic.

A Lévy-walk/ f l ight , named af ter the French mathematician Paul Pierre Lévy, is a type of random walk in which the probability distribution of flight length l follows a power law of the form

In [14-15], the authors report that humans walk a Levy-walk of α = [0.53, 1.81].

In our paper, we use the Levy-walk model to detect wandering which is one of the early symptoms of dementia.

Figure 2 Sensing Procedure with the Ultrasonic Sensor

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3 Proposed Silver-Care System for Early Dementia Patients

3.1 System OverviewOur system for wandering and activity recognition

involves the use of an accelerometer and ultrasonic sensors. We tested our system at the Eden Adventist Hospital. Figure 3 shows the architecture of the test bed and the structural view of the inside of the Eden Elderly Care Center. Our system was deployed on the 3rd floor of the hospital, which consists of an aisle, personal rooms and lobby. The aisle runs along the long side of the building and the personal rooms are on the opposite side along the aisle. The lobby is located in the middle of the floor and is used for rest and small activities.

There were about 30 elderly patients in the group, which included both dementia patients and non-dementia patients. We tried to gather data from dementia patients, but they typically do not like to wear sensor devices on the body. Continuous training enabled the attachment of five ultrasound sensors for location tracking of nine patients, respectively. Nine patients were early dementia patients and the others were non-dementia patients.

We attached an accelerometer and ultrasonic sensors to people to collect activity data and gather location information, respectively. The service server collected the data and triggered a wandering event from the elderly dementia patients. Medical social workers, nurses and family were alerted in response to wandering events to allow proactive treatment of the patients.

Figure 3 System Architecture

3.2 System Network Environment for Activi ty Determination We created a sensor network environment to test the

proposed tri-axis acceleration sensor system. As mentioned above, our attempts to attach sensor devices to the dementia patients did not work. This experiment was executed in the laboratory. The sensor node was developed by iWare.Co., Ltd. in Korea (see Figure 4).

Figure 4 iWare Sensor NodeThe sensor node consisted of MSP430 for MCU,

CC2420 for wireless communication, six 3-axis acceleration sensors and three MUXs to select and receive data from the sensors. Figure 5 shows the hardware structure of the sensor node with only three acceleration sensors.

The sensor node was ported based on the TinyOS 1.x application. At the beginning, channel selection signals from the MSP430 were sent to MUXs. MUXs select the first channel of the 3-axis acceleration sensor, which is channel 0, the first acceleration module. Each axis of the channel 0 sensor sends data.

The corresponding x-, y-, and z-axis values are packed and transmitted by CC2420. After the packet of channel 0 is transmitted, the sensor node changes to channel 1. The channel 1 sensor sends packets in the same way as the channel 0 sensor. The same operations are then repeated up to the sixth channel. Figure 6 illustrates the flow of the software system.

Figure 5 Hardware Structure of a Tri-axis Acceleration Sensor

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Figure 6 Software System Flow

We experimented by wearing the sensor node as shown in Figure 7. Six 3-axis acceleration sensors are located on the neck, both shoulders, both wrists and middle of the waist to check for movements.

Figure 7 Wearing the Sensor Node

We simulated two early dementia symptoms: repetitive unlocking and locking of buttons and repetitive picking things up and placing into a pocket. Figures 8, 9 and 10 are show representations of different activities: normal standing, repeatedly unlocking and locking buttons and picking things up into the pocket, respectively. The legends of the x/y axis’s in Figure 8, 9 and 10 are time and respective value, x-, y-, and z-axis values of a tri-axis acceleration sensor.

In the normal standing status (see Figure 6), the z-axis shakes a little, but the signals seem to maintain a constant pattern.

In Figure 9, the pattern for repeatedly unlocking buttons appears as a big “W” shape involving all 3 axes (x, y and z). The action of repeatedly picking things up produced a highly irregular pattern due to the whole body moving (see Figure 10).

From these experiments, we collected data related with repetitive activities such as picking up, unlocking and locking buttons, etc. We can use this information to improve the system and create an algorithm to detect activity patterns of early dementia patients in future work.

3.3 Algorithm for Detecting LocationWe used the system described above to determine the

exact current geographical location of eldery people. There are many kinds of location tracking methods such as RF signal strength, UWB, etc. We have experimented with location tracking using RSSI (Received Signal Strength Indication/Indicator) in a wireless sensor network. We

Figure 8 Normally Standing Status

Figure 9 Repeatedly Unlocking and Locking Buttons

Figure 10 Repeatedly Picking Up Things

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used Zigbee sensor motes, which were developed by the Hanback Electronics Corp. Research Center in Korea. They are based on MSP430, and are equipped with a CC2420 RF chip and are ported using the TinyOS 1.x application. But, it is difficult to detect an exact location. Accurate location information is essential for location tracking. Such ultrasonic tracking systems are widely used for indoor location [16-17] and relative positioning [18]. Because of the reasons mentioned above, we provide a location detecting algorithm using ultrasonic sensors.

Figure 11 shows the attachment of ultrasonic sensors to the human body and receivers to the walls at the hospital in order to detect the precise coordinator of patients.

A quadrangle represents the personal room and the four black points in the corners are listeners for ultrasonic sensors, as shown in Figure 12. We calculated the location of a patient using two distance values from the listener of the ultrasonic sensors, as 3 distances are not always

available due to the potential blocking of ultrasonic waves by small obstacles. In the following equation with two points, (x, y) represents the location of the patient where (x1, y1) and (x2, y2) are the coordinates of the center of each circle.

(1) (2) (3) (4)

By substituting Equations (3) and (4) into Equations (1) and (2), respectively, the new derived equation is as follows:

(5) (6)

When we compute Equations (5) and (6), we get the simplified equation below:

(7)

By simplifying Equation (7), we can get the following Equation (8).

(8)

(9)

Finally, we get the accurate coordinates for the patient location by substituting Equation (9) into Equation (8).

(10)

(11)

3.4 Wandering Activity Estimation3.4.1 Location Tracking

We mentioned in Subsection 2.4 that human mobility can be described by the Levy-walk model. Thus, we gathered location information from early dementia patients, traced their wandering patterns, and analyzed them using the Levy-walk model. Approximately from 30 patients, location data of 9 patients were collected and a Figure 13 shows that location tracking data samples collected from four patients during 24 hours in Eden Elderly Sanitarium Center.

Two patients’ movements, (a) and (b) in Figure 13, are similar that they usually move to limited spaces which are his rooms, toilet and resting room for meals and resting.

Figure 11 Test Bed

Figure 12 Calculation of Location Using Two Distance Values

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Especially, (a) Mr. Yoo (84, M) used to often go toilet. The movement which seems to be wandering, (c) Mrs. Kim (82, F), is more complex, because she visits diverse spaces where are not related with her and stay.

After gathering the location information, we chose two out of the 9 patients for further analysis. One of the two patients had early dementia symptoms and the other was a normal control patient (Figure 14).

Figure 14 Normal and Suspected Case

Based on collected patients’ location information, we developed the hypothesis that a patient suspected of having early dementia would follow the Random Way Point model and a normal control case would follow the Levy-walk model.

We identified and tracked all movements from the data of a person suspected to have early dementia on the cross-sectional diagram of the 3rd floor of the Eden Center. We also followed the movements of a normal case for comparison, as shown in Figure 15.

Figure 15 Location Tracking

There was a disctinct pattern of movement with the dementia patient, who went to more locations than the normal case. In interpreting these results, we kept in mind the following different properties of human mobility and wandering.

y Human mobility generally has a specific visiting purpose. Thus, humans spend certain amounts of time at a single place. y A case of dementia with memory loss could induce frequent wandering, even if the patient appears to seek places with clear purpose such as the bedroom, bathroom and dining room.

Based on the above two properties, we found distinct difference in the movements: “Purpose”-based movements in the normal case stay at a place for a meaningful amount of time. Hence, we included a time domain in the model for this problem.3.4.2 Time Graph

Figure 16 demonstrates the pattern of staying in a particular place under the same time zone as in Figure 15. Positions away from the center of the circle indicate more time spent in that place. Then, the ratio of the observed target location, based on connecting each point, is represented by polygons. According to the “normal case” in Figure 16, the person who lives in room number 304 does not have appear to wander, but rather spends most of the time in his room and visits other rooms for a few hours at a time.

However, for the person who has the symptom of wandering, the daily living pattern is shown as the

Figure 13 Location Tracking Data

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“suspected (wandering) case” in Figure 16. Both cases produce different patterns; the “normal case” is represented by an acute angle polygon, while the “suspected (wandering) case” is represented by an obtuse polygon on the whole. The ratio of visited places aside from room 304 is higher for the “wandering case” compared to the normal case.3.4.3 Wandering Detection

To establish an exact detection model, we calibrate the two data sets in terms of geographical location information and amount of time spent at the location, as the time domain is intimately linked with mobility, especially for wandering detection. So we examined this strong association using Figures 15 and 16. We derived a combined model, as shown in Figure 17, which describes location tracking information with staying period. The circles represent staying period in particular rooms.

The dotted circles of a Levy-walk are regional movements in particular area. To put it another way, they represent “Purpose”-based movements that include a time domain. To accurately detect the wandering pattern, we need to consider meaningful behaviors such as reading, talking, and washing in order to employ the average amount of time spent for the specific behaviors at the particular places.

Figure 18 shows different amounts of time (also denoted to exceeding time) compared with the average staying time at particular places for each patient. A position away from the center of the circle indicates a long time spent in that place. The inside dotted small circle is the daily average period for staying at each place and the short bar means variation around the average value. The wandering has particular relevance to the exceeding time expressed as A in Figure 18.

We denote the number of rooms as n and rate of the ith room as Ri. Then, the total area of polygon A is calculated based on Equations (12) and (13).

(12)

(13)

To apply the Levy-walk model discussed in Section 2.4 to our data, where α = [0.53, 1.81] for normal movements referred in [14], the sum of A in our model denoted as Atotal, needs to be converted to the r value in the Levy-walk model.

(14)

Table 1 shows the daily changing ratio of exceeding time of the normal case, and finally Atotal is calculated by heuristically β is observed by 15. With this observation, the total ratio range of exceeding time covered 0.53 ≦ Atotal ≦ 1.81. Under these conditions, we can detect the wandering pattern by using the Levy-walk model if Atotal is greater than 1.813.

Table 2 shows that the exceeding time of the suspected case is perfectly indicative of a random walk from Figure 17, where Atotal is greater than 1.81.

Therefore, our model and proposed activity detection system based on a combination of location and time information from human movements can be successfully applied with the statistical Levy-walk model to detect wandering events.

Figure 16 Period at a Particular Place

Figure 17 Location Tracking with Information on Staying Periods

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3.4.4 Wandering Detection EvaluationIn this subsection we are about to estimate the

confidence interval. Table 3 includes the general information of patients and At_exceed values from the data of the 9 patients who participated in the experiment.

Table 3 Values At_exceed for all Patients

No. Name Sex At_exceed Mobility Pattern1 Mrs. Kim F 4.18 RW2 Mrs. Hwang F 3.76 RW3 Mr. Kim M 3.17 RW4 Mrs. Son F 3.1 RW5 Mr. Kim M 2.99 RW

Critical Value 2 - 2.96 Mr. Yoo M 1.95 LW7 Mrs. Lee F 1.52 LW8 Mr. Yoo M 0.93 LW9 Mr. Park M 0.86 LW

Critical Value 1 - 0.85

The 95% confidence interval of the sampling unit calculated using the average, a normal case = 1.315, a suspected case = 3.44, from Figure 19 are indicated below (Equation [15]).

Figure 19 Distribution of RWP Samples in 95% Confidence Interval

(15)

where, Var(x) is variance and u is average of suspected cases.

Through the Equation (15), 95% confidence interval on the normal distribution we hypothesized from sample group is calculated by (2.45, 4.44) as shown in Figure 19. Hence, we observed the error by 0.45 compared to the critical value, 2.9, from the Levy’s distribution to trigger RWP in the 95% confidence interval. Due to some difficulties in collecting many samples such as taking away their sensors

Figure 18 Digitized Wandering Pattern

Table 1 Value Atotal for Normal Case

At_exceed 0.85 … 1.52 2.9At_exceed 0.0354 … 0.063 0.121At_daily

Atotal 0.5313 … 0.95 1.813

Table 2 Value Atotal for Suspected Case

At_exceed 0.85 … 0.29 3.17At_exceed 0.0354 … 0.121 0.132At_daily

Atotal 0.5313 … 1.813 1.981

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or even hiding them, few samples are gathered, which makes it hard to estimate wandering detection accurately within the confidence interval of the normal distribution. However, observed samples from the Eden Center well follow our wandering detection model by applying Levy-walk model.

4 Conclusion

In this paper, we propose a technique to detect patterns of wandering as an early symptom of dementia. We developed a system to digitize activity using acceleration sensors. We also proposed an algorithm using two schemes, location tracking and time graph, to detect wandering by applying the Levy-walk model for early assessment of the risk of dementia. We then performed experiments to verify our algorithm and hypothesis.

To increase the practical use of our activity recognition system, more exquisite algorithms are needed to distinguish running from walking and sitting from standing and lying. In the future, we will study more complex hardware and sophisticated algorithms for high-level activity recognition. Also, we will install a test bed in a nursing home to practically solve the problem in a real-life scenario.

Acknowledgements

This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (No. 2011-0003932) and by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2011-C1090-1121-0003).

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Biographies

Sang-Ho Na earned BS and MS degrees from Kyung Hee University, Seoul, Korea, in 2008 and 2010, respectively. He is currently a PhD Candidate in the Department of Computer Engineering, Kyung Hee University, Korea. His research interests include uHealthcar

system based on Wireless Sensor Network, Security of Wireless Sensor Network, Cloud Security.

Kyu-Jin Kim earned BS and MS degrees from Kyung Hee University, Seoul, Korea, in 2008 and 2010, respectively. His research interests include Wireless Sensor Network, u-Healthcare.

Eui-Nam Huh earned a BS degree from Busan National University in Korea, a Master’s degree in Computer Science from the University of Texas, USA in 1995, and a PhD degree from the Ohio University, USA in 2002. He is now a Professor in the Department of Computer

Engineering, Kyung Hee University, South Korea. His research interests include Cloud computing, high performance networks, sensor networks, distributed real-time systems, and security.

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