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Received March 13, 2016, accepted April 6, 2016, date of publication April 13, 2016, date of current version April 29, 2016. Digital Object Identifier 10.1109/ACCESS.2016.2553838

WristEye: Wrist-Wearable Devices and a System for Supporting Elderly Computer Learners LIANG-BI CHEN1 , (Senior Member, IEEE), HONG-YUAN LI2 , WAN-JUNG CHANG2 , JING-JOU TANG2 , AND KATHERINE SHU-MIN LI1 , (Senior Member, IEEE) 1 Department 2 Department

of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan Electronic Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan

Corresponding author: W.-J. Chang ([email protected]) This work was supported by the Ministry of Science and Technology under Grant MOST-104-2220-E-110-007, Grant MOST-104-2220-E-218-001, and Grant MOST-104-2220-E-218-002.

ABSTRACT Today, there is an increase in the number of elderly people who want to start using computers and the Internet. However, elderly people are hindered by limited education, limited knowledge of digital information, words they do not know, and not knowing how to use the computer keyboard and mouse. The setbacks and frustration they face in learning to use the computer causes them to feel computer anxiety. This paper proposes a computer-learning-assisted system equipped with wrist-wearable devices to help elderly computer learners as they perform computer learning tasks. This system, named the WristEyesystem, can discern and analyze learners’ attitudes, reactions, and behaviors as they participate in computer literacy classes. In the WristEyesystem, a kinematic sensor attached to a student’s wrist can detect differences in wrist orientation and vertical acceleration and determine which learning computer operations are in process, i.e., directing the mouse, hitting the keyboard, idle, and random undirected movement of the mouse. Moreover, a remote backend server receives the detected signal from the wearable unit by a wireless sensor network and then analyzes the corresponding computer learning effectiveness to produce results in graphic and score form to an instructor who can use this information to better tailor his lessons and activities to the needs of the learners. INDEX TERMS Computer-learning-assisted system, kinematic sensors, learning technologies, wearable technologies.

I. INTRODUCTION

The past years have seen an increase in number of computer users in some countries with aging populations, including the U.S. [1] and the U.K. [2]. The West is not alone. In Japan, the percentage of the population greater than 65 years of age grew from 15.1% in 1996 to 21.8% in 2008 [3], [4]. Computers can improve the life quality of elderly people in many ways, for example by expanding their ability to shop online, connect socially, and continue learning, etc. [5]. This may be of importance to those who have retired and find themselves at home isolated with little to do. Even if they are not interested, elderly people may find themselves left behind society as a whole as many of their countries implement a wide range of e-government projects (e.g., e-Japan in Japan, A Framework for Global EC in America, UK Online in England, and e-Korea in Korea) hoping to integrate the use of advanced information technology (IT) throughout their nations [6]. Moreover, better health enables older people to remain longer

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in the workforce, where they may encounter adjustment difficulties because computers are being introduced to an increasing number of jobs [7]. Therefore, there is a quickly growing need to train older adults in basic computer skills. Basic operations such as use of keyboard and mouse are necessary when interacting with computers. Research has revealed that older adults spend more time and go through more trial and error when acquiring the basic computer skills [8]. Even simple operations like holding and clicking the mouse at the same time trouble older computer users [9]. Hence, training older adults to successfully perform basic computer operations is a good starting point, especially at the beginning when frustration levels and computer anxiety are high [10]–[12]. Wolfson et al. [7] has suggested that a structured training program with feedback and adaptive guidance would be very helpful in educating older adults. This has been found to be especially true when it comes to learning new technologies [21].

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Wearable devices, such as wristbands, smart watches, are gaining in popularity. Into such devices can be embedded a variety of sensors which can give birth to a number of diverse functions. Our team wanted to develop an assisted learning system incorporating a wearable device that would be able monitor first-time learners’ use of mouse and keyboard and provide their instructors with useable feedback. We created a wrist-worn sensing device, the WristEye unit, which through a ZigBee Wireless Network sends performance time and wrist movement data to an instructor’s base station where they are analyzed in a backend server. An original algorithm was developed to trace the trajectory of the user’s wrist and evaluate a student’s performance based on how closely and how quickly his performance matches that of his instructor. Real-time results are shown on the instructor’s monitor in easy-to-read graphical and numerical form. We have applied the use of this system to a class of elderly computer learners to evaluate its accuracy and find out if and how it could be used to evaluate the performance of the learners as they are being introduced to keyboard and mouse usage. It was found to provide immediate accurate feedback to the instructor on the learners’ ongoing performance of tasks and found to show instructor which students need more one-toone help with which operation in any given class session. The remainder of this paper is organized as follows: Section 2 of this paper describes the previous works on the design and development of wearable devices for educational purposes. Section 3 describes the design and architecture of our WristEye system and its analysis. Section 4 describes an experiment performed with elderly computer learners to try out the system and test its accuracy, Section 5 presents the results and analysis of our experimental data, and Section 6 presents our conclusions. II. RELATED WORKS

The use of wearable devices as part of an integrated IT system is becoming increasing common for a variety of purposes [22], [23]. These have come in belt and wrist ring forms [24]. Their increase in use comes in tandem with the exponential development of microelectronics, which makes chips smaller and smaller and more powerful as well as the emergence of an increasing number of diverse sensors enabling computer systems to obtain a greater variety of environmental information. Wearable devices incorporating these technologies are beginning to be used for educational purposes to aide or evaluate learner performance [25]–[27], [31], [35]. The studies of these educational uses are few in number, but show promise. Balestrini et al. [25] proposed a signal orchestration system (SOS) consisting of several wearable devices. In that particular study of the use of SOS to enhance performance of a jigsaw activity, the students’ actions were orchestrated by simultaneous audio-visual signaling to all team members wearing armband devices. All participants wearing the device had access to the same ongoing changes in activity status data. It was found that students equipped VOLUME 4, 2016

with wearable-device-based SOS system performed the activity more efficiently than those in another group using a traditional paper-based method of orchestration. SOS users required less time to organize and achieved higher scores. Their SOS group also reported a greater sense teamwork. CaReflect, an app collecting data from proximity sensors in wearable devices, and WATCHiT, a wearable computer for the collection of data in crisis management, were utilized by Müller et al. [26] in their research investigating use of these devices to help trainees reflect on their own performance in certain work situations. The efficacies of the wearable devices were evaluated for their effect on reflective learning in trainees involved in dementia care and crisis management in two studies, respectively. While both studies found attitude toward the systems to vary, they did both find the wearable devices to enhance reflective learning in trainees in both groups. Nakasugi and Yamauchi [27] developed a system involving the use of a wearable computer to facilitate the study of history. Their proposed system presents students with a vivid visual experience of the historical incidents which they could compare with their understanding of the events. The authors claimed that the wearable device would more highly motivate users to study history by presenting them with an enhanced visual experience. In summary, studies developing wearable devices as a component of computer-assisted learning and experimenting with their uses in learning situations are few and varied. However, their results have been promising. We believe the use of such devices for educational purposes will continue to grow. III. THE PROPOSED WRISTEYE SYSTEM A. OVERVIEW OF THE PROPOSED WRISTEYE SYSTEM

The purpose of the proposed WristEye system developed for this study was to capture the real-time learner performance of basic computer operation learning tasks by recording learners’ directed and random undirected mouse movements, key hits, and idleness. For this, we designed a small wrist-worn device with kinematic sensors capable of low-power dissipation and wireless communication. The device was used to monitor the wrist movements of elderly computer learners as they attempted to perform basic computer operations introduced and assigned as learning tasks by an instructor. The architecture of the proposed WristEye system is shown in Fig. 1. The proposed WristEye system consists of wearable unit, ZigBee wireless access point (AP), and a base station (as can be seen in Fig. 2) connected by a RS-232 cable to a backend server. As can be seen, the wearable unit consists of kinematic sensor, microcontroller (MCU), and transceiver. This unit detects the learner’s wrist movements and processes the signals. After processing the signals and identifying wrist movement behavior, it transmits this information via a ZigBee wireless AP to another transceiver where the data is further processed and sent by RS-232 cable to a backend server. The backend server contains software that can compare the keyboard and mouse movements of the learners with 1455

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FIGURE 3. Assemblage of wearable wrist unit.

FIGURE 1. Proposed WristEye system.

FIGURE 2. Base station.

those of the instructor and produce a real-time result showing the degree of similarity between the two. Closer approximation indicates more correct learner usage and greater dissimilarity indicates more learner difficulty. B. WEARABLE UNIT IMPLEMENTATION

A photograph of the unit worn around the wrist is shown in Fig. 3. The kinematic sensor is comprised of an IDG300 gyroscope [33] and an ADXL330 accelerometer [34]. The data acquisition board contains an Atmel Atmega 128L MCU, 4 KB of RAM, various ICs and a wireless transceiver module. The sensor unit has two main functions. The first function is to detect changes in the wrist posture and movements of the wearer. The second function is to merge the data signals of the accelerometer and gyroscope before transmitting data to the base station. The sensor unit is equipped with a low-voltage, low-power consumption ZigBee-compliant transceiver module, which facilitates wireless communication with the base station. 1456

C. WRIST RECOGNITION ALGORITHM DESIGN

After receiving the wrist orientation (across the surface) and vertical acceleration (nearness and farness from surface) (3D) data from kinematic sensor, the MCU within the wearable wrist unit utilizes a wrist movement recognition algorithm to identify the corresponding computer learning activities. The algorithm commences by applying vertical wrist acceleration threshold value to determine whether the wrist movement of the subject can be categorized as ‘‘moving mouse’’ or ‘‘hitting keyboard’’. For example, when the subject changes from a ‘‘moving mouse’’ to a ‘‘hitting keyboard’’ computerlearning activity, the vertical movement of his wrist exceeds its vertical threshold because it will invariably move up and down vertically as it hits the keyboard keying information letter-by-letter. This vertical wrist motion is classified as ‘‘hitting keyboard’’. Conversely, when moving the mouse, the wrist tends to move horizontally and thus vertical acceleration would be less than the critical vertical value. The corresponding wrist motion would be classified as ‘‘moving mouse’’. The acceleration profile associated with the moving mouse activity has a lower vertical acceleration than that of hitting keyboard. Thus, the wrist recognition algorithm developed in this study exploits characteristics of the acceleration profiles to distinguish the four motions. Fig. 4 illustrates the details of the posture recognition algorithm in flowchart form. The difference in kinetic energy of wrist when per-forming mouse operations and keyboard operations is significant. When elderly people attempt to learn how to use a computer, there are not only mouse and keyboard operations expressed by the movement of their wrists, there are also stillness or idleness when they are thinking how to perform a task and random undirected mouse movement when they do not understand what to do, which can also be expressed by the movement or non-movement of their wrists. Therefore, to account for these movements, we mapped the data of X axis, Y axis and Z axis to their real-time computer operations and introduced an algorithm in Fig. 4 for the recognition of these four types of activities expressed by the wrist. They are: 1) keyboard entering, 2) directing the mouse, 3) undirected moving of the mouse and 4) idling. In addition, in order VOLUME 4, 2016

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FIGURE 5. Graphic display of operations performed on instructor’s monitor (backend server). FIGURE 4. Wrist recognition algorithm.

to avoid ‘‘noise’’ caused by frequent recognition short time micro changes in kinetic energy values, the number of events of every computer operation over a specified time was collected and entered into the algorithm to perform sequential decision-tree-like functions in the recognition of different operations. The prior probability (p) of each kind of computer operation is derived from the ac-cumulated number of each kind of event occurring within a specified sample time. Finally, the posterior probability (P) of an event from (1) is defined as: P = α · c + (1 − α) · p

(1)

where c is the occurrence probability of the event and is regularization parameter between 0 and 1. The output of recognition algorithm is the operation event with maximum P. Our proposed method not only recognizes the exact operation behaviors of elderly computer learners, but it also estimates the order of operation events and duration to evaluate the learning performance of the learner. The performance of the proposed WristEye system was quantified by comparing the movements of the actual performance of computer task by the elderly learners with those identified by the wrist recognition algorithm, as can be seen in Fig. 5. IV. PRACTICE DESIGN AND EXPERIMENTAL RESULTS A. WRISTEYE SYSTEM INSTALLATION

The WristEye system was installed in an educational institute in Taiwan. There a class of thirty elderly students was asked to wear the sensing wearable units as they participated in computer workshop sessions. The sessions were led by an instructor who received the real-time student performance feedback on his monitor via the WristEye system. Fig. 6 shows the positions of the various components in the classroom: the wearable units, the ZigBee wireless network, VOLUME 4, 2016

FIGURE 6. Schema of classroom system configuration.

the base station, the backend server (instructor’s computer). The routers are also shown. Fig. 7(a) shows the schema of entire computer operations classroom, and Fig. 7(b) shows a close up of learners wearing the units and practicing computer operations. B. PRACTICE DESIGN

There are many sequential operations performed using both the keyboard and the mouse, each expressed differently by the horizontal and vertical movements of the wrist. Fig. 8(a) shows the dynamic kinetic data (raw data) detected by the sensor as the learner keys in data or uses his mouse. The blue line represents horizontal movement (mouse use), and the pink line represents vertical movement (keyboard use). Fig. 8(b) is a photo of the wearable wrist unit as it is worn by a learner. The following wrist movements needed to be closely examined and characterized: hitting the keyboard, idle-ness, 1457

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FIGURE 7. Learners in action.

FIGURE 8. (a) Graphic depiction horizontal (mouse) and vertical (keyboard) movement when executing a Google search. (b) Photo of wearable unit as it is worn by a learner.

FIGURE 9. Steps in performing a Google search.

directed use of the mouse, and undirected movement of the mouse. Fig. 8(a) depicts in graph form the signal measurements obtained by the kinematic sensor of wrist 1458

vertical movement while the subject executes a simple computer operation, in this case a Google search, shown in Fig. 9. The operation was executed as follows: VOLUME 4, 2016

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Step 1: Move the mouse until the pointer touches the search engine icon (e.g., Chrome, Explorer, etc.) and click. Move the mouse until the pointer touches the search engine search inquiry box and click (Fig. 9(a)). Step 2: Insert a search word by hitting the alphabet keys on the keyboard spelling out the search that word (e-learning) (Fig. 9(b)). Step 3: Move the mouse until the pointer touches the first search result and click (Fig. 9(c)). To program the system for this Google search activity, the instructor first puts on wearable unit and performs the operations needed to execute the Google search while the software in the educator’s computer records the kinetic data and stores it as the target sample. Once entered, the students are assigned to compete the Google search within 30 seconds.

FIGURE 10. Graphic display of the wrist movements of the instructor vs. three students as they execute a Google search.

Fig. 10 illustrates how the graphs are designed to show the reading of the wrist movements of the instructor and three elderly students executing a Google search activity. As can be seen in the graphs, Student A can successfully complete

SADi =

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   Tj − Sij ,

the assigned step sequence, though it takes him longer to key in the search word. This result shows that he has learned well. Student B correctly performs the initially but encounters difficulty when keying in the search word. He is unable to complete all the steps within the specified time. Student C starts out incorrectly and is unable for follow the procedures or complete the exercise. C. WRIST KINETIC PATTERNS DESIGN

We identified the pattern differences in terms of type of operations and time used between the instructors and the learner by analyzing differences in kinetic energy data collected by the kinematic sensor in their wrist units. These patterns of kinetic energy of wrist were defined as Reference Pattern (RP) and Alignment Pattern (AP) for teacher and student, respectively. Comparing the switching from mouse to keyboard and time used to perform these operations expressed in the RP and AP, we were able obtain degree of similarity degree in the patterns (SADi ) between teacher and student as (2), shown at the bottom of this page, where Ti is the jth computer operation event order from the RP obtained from the instructor and Sij is the jth computer operation event order from AP obtained from ith student. The smaller the SADi value, the better the student’s performance. A fuzzy evaluation equation was introduced based on the learning performance evaluation criteria (Gi ). The closer a learner approximates the maximum score of 100, the better the learning performance of ith student (SADi is also as smaller). The fuzzy evaluation equation (3) as calculated as follows: 1 × 100, x ∈ [0, ∞) (3) Gi = 1+x where SADi x= (4) Total time used by instructor to perform operation Fig.11 shows the shape of Z-type function of SADi divided by 100. This figure shows if SADi is 100, then Gi is 50, a result of the large difference in operation time or the order of mouse-keyboard operations between teacher and ith student. Once all of Gi values are computed, a Fuzzy Rule Algorithm to evaluate learning performance is introduced, shown a follows: Fuzzy Rule Algorithm: Step 1: If Gi ≥ 90, then the learning performance of ith student is very good; Step 2: If 80 ≤ Gi < 90, then the learning performance of ith student is good; Step 3: If 60 ≤ Gi < 80, then the learning performance of ith student is average;

if the pattern of ith student and teacher are match in wrist status of jth computer operation order, j=0    max Tj , Sij , otherwise.

Xn

(2)

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FIGURE 11. The shape of Z-type function of SADi divided by 100.

Step 4: If 50 ≤ Gi < 60, then the learning performance of ith student is poor; Step 5: If Gi < 50, then the learning performance of ith student is very poor. V. ASSESSMENT AND ANALYSIS OF EXPERIMENTAL RESULTS

To find out if the system can distinguish different abilities to use computer, we compared data from three elderly students with that of the instructor, an instructor at a computer education institute. Student A: Good skill and good understanding of task. Student B: Good skill but inferior understanding of task. Student C: Poor skill and poor understanding of task. The differences between the instructor’s sample performance and the performances of the three students can be seen in Fig. 12. Student A, considered a good performer of the task, took a longer time than the instructor to finish his work, but the steps he took totally matched those in the instructor’s sample. Student B, considered an average performer of the task, moved his mouse correctly, but encountered difficulties using the keyboard. He failed to finish his assigned task within the specified time. Student C, considered a very poor performer of the task, started out incorrectly using the keyboard instead of the mouse, swung his wrist widely, between his first incorrect steps and the following steps. Ultimately, he was unable complete the task. Table 1 summarizes the quantitative results the instructor’s sample and students’ performances. Using SADi , we can quantify learner skill. Student A demonstrated the best skill as evidenced by a minimum SADi , which indicates his skill at manipulating the computer closely resembled his instructor’s. Student B exhibited moderately good skill but at this point he is not good at keyboard use, and thus the higher SADi . Student C, the least skillful, had the highest SADi number. Entering SADi into (3), we can obtain a learner’s Gi , an indication of understanding of the learning task. From a learner’s Gi , the instructor can determine whether his 1460

FIGURE 12. Kinetic models of the instructor and three students performing a learning task, executing a Google search.

understanding is good, average or poor. In addition to rating the students’ performance of the learning task, the teacher, once familiar with the system reports, can also ascertain which operations individual students are having most difficulty with and better focus his teaching efforts on individual needs, giving the weakest more attention when they need it the most. He could also assign a better performing learner to assist a poorer performing learner. To further test the selectivity of the WristEye system, we used it to categorize students into three classes (A, B, and C) of 30 students each based on their abilities. Fig. 13 shows the time it took the students to perform the assigned sequence of tasks on the computer. Mean duration as well as standard deviation and confidence interval were calculated, with a margin of error of plus or minus 5 percent. VOLUME 4, 2016

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TABLE 1. Performance of Three Learners Based on the Wristeye System.

FIGURE 13. Sequence performance time.

TABLE 2. Performance of Three Classes of Learners Placed Using the Wristeye System.

As can be seen in Table 2, a summary of mean task performance scores for three classes of students placed by WristEye system SAD results, Class A had a mean SAD score >80, indicating they had the best performance. Class A performance of tasks more closely resembled the performance of their instructor’s than the performances of VOLUME 4, 2016

Class B or C. Group B had a mean SAD score of 62, mostly due to keyboard difficulty. Class C had a mean SAD score of 49.5, indicating poor task performance in both keyboard and mouse use. These learners would probably need individual attention from their instructors. If they belonged to a multi-level groups of learners, those with scores around 1461

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80 could be assigned to help those with scores around 49.5. This would save the instructor time, speed up the class, and avoid some ‘‘public’’ embarrassment. VI. CONCLUSION

The WristEye system introduced is this study consists of a wearable unit equipped with kinetic movement sensors and a transmitter that sends individual student performance data via WSN to a central classroom computer station containing software that compares a learner’s performance of assigned computer tasks with the instructor’s performance of the same tasks. Performance data are displayed real-time in graphic form and evaluated real-time by a newly developed algorithm for the instructor. The system was tested in one group of elderly computer learners of varying abilities and tested in three classes of learners, each class with a different skill level. It was concluded that the wrist-wearable computer learning assisted learning WristEye system, accurately described and assessed the performance of assigned learning tasks. The results produced by this system can be used to better tailor educational activities to the needs of learners, in the case of this study, elderly computer learners. ACKNOWLEDGMENT

The authors would like to thank Dr. Tian-Hsiang Huang from the Wireless Broadband Communication Protocol CrossCampus Research Center, National Sun Yat-Sen University, Kaohsiung, Taiwan, for assisting mathematical theory definition and discussion. The authors also would like to thank Mr. James Steed editing the English of the draft. REFERENCES [1] A. Smith, ‘‘Older adults and technology use,’’ Pew Res. Center, Washington, DC, USA, Tech. Rep., 2014. [2] Statistical Bulletin: Internet Access—Households and Individuals, Office Nat. Statist., U.K., 2015. [3] Ministry of Internal Affairs and Communication. Statistics Bureau and the Director-General for Policy Planning (Statistical Standards), accessed on Sep. 27, 2015. [Online]. Available: http://www.stat.go.jp/english/index.htm [4] Boston Globe. Japan Eyes Robots to Support Aging Population, accessed on Sep. 27, 2015. [Online]. Available: http://www.boston.com/news/world/asia/articles/2007/09/18/ japan_eyes_robots_to_support_aging_population/ [5] H. Yoon, Y. Jang, and B. Xie, ‘‘Computer use and computer anxiety in older Korean Americans,’’ J. Appl. Gerontol., pp. 1–11, Feb. 2015. [6] (2000). Jupiter Communications. [Online]. Available: http://www.jup.com [7] N. E. Wolfson, T. M. Cavanagh, and K. Kraiger, ‘‘Older adults and technology-based instruction: Optimizing learning outcomes and transfer,’’ Acad. Manage. Learn. Edu., vol. 13, no. 1, pp. 26–44, 2013. [8] K. V. Echt, R. W. Morrell, and D. C. Park, ‘‘Effects of age and training formats on basic computer skill acquisition in older adults,’’ Edu. Gerontol., vol. 24, no. 1, pp. 3–25, 1998. [9] D. Hawthorn, ‘‘Possible implications of aging for interface designers,’’ Interact. Comput., vol. 12, no. 5, pp. 507–528, 2000. [10] J. C. Marquié, L. Jourdan-Boddaert, and N. Huet, ‘‘Do older adults underestimate their actual computer knowledge?’’ Behaviour Inf. Technol., vol. 21, no. 4, pp. 273–280, 2002. [11] S. J. Czaja and C. C. Lee, The Human-Computer Interaction Handbook, J. A. Jacko and A. Sears, Eds. Hillsdale, NJ, USA: L. Erlbaum Associates Inc., 2003, pp. 413–427.

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LIANG-BI CHEN (S’04–M’10–SM’16) received the B.S. and M.S. degrees in electronics engineering from the National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, in 2001 and 2003, respectively. He is currently pursuing the Ph.D. degree in computer science and engineering with the National Sun Yat-sen University, Kaohsiung, Taiwan. From 2004 to 2011, he served as a Teaching and Research Assistant with National Sun Yat-sen University. Since 2004, he has served as an Adjunct Lecturer with National Sun Yat-sen University, the National Taichung University of Science and Technology, the National Pingtung University of Science and Technology, National Pingtung University, Tajen University, Kun Shan University, Shih-Chien University Kaohsiung Campus, and Meiho University, Taiwan. Since 2004, he has also been an Adjunct Teacher with the Kaohsiung Municipal Kaohsiung Industrial High School, the Kaohsiung Municipal Kaohsiung High School of Commerce, and the Kaohsiung Municipal ChungCheng Industrial High School, Kaohsiung. In 2008, he had an internship with the Department of Computer Science, National University of Singapore, Singapore. He was also a Visiting Researcher with the Department of Computer Science, University of California, Irvine, CA, USA, from 2008 to 2009, and with the Department of Computer Science and Engineering, Waseda University, Tokyo, Japan, in 2010. In 2012, he joined BXB Electronics Company, Ltd., Kaohsiung, as an R&D Engineer. In 2013, he was transferred to Executive Assistant to Vice President. His research interests include VLSI design, power/performance analysis for embedded mobile applications and devices, power-aware embedded systems design, low-power systems design, digital audio signal processing, engineering education, project-based learning education, SoC/NoC verification, and system-level design space exploration. Since 2013, he has served as a Section Editor Leader, an Associate Editor, and a Guest Editor-in-Chief of the IEEE Technology and Engineering Education. He has also served as a TPC Member, an IPC Member, and a Reviewer for many IEEE/ACM international conferences and journals. He has led many student teams to win more than 20 awards in national/international contests. He was a recipient of the 2014 IEEE Education Society Student Leadership Award and the 2015 IICM S.M. Cho IT Student Leader Award. He is a member of IEICE and PMI.

HONG-YUAN LI received the master’s degree in psychology and the bachelor’s degree in electronics from East China Normal University. He is currently pursuing the Ph.D. degree in electronics engineering with the Southern Taiwan University of Science and Technology, Tainan, Taiwan. He is also a Lecturer with the School of Computer Science, Shanghai Jian Qiao University, China. His main research interests include vehicular ad-hoc networks and embedded systems. He has participated in national projects by the Chinese National Fund.

VOLUME 4, 2016

WAN-JUNG CHANG received the B.S. degree in electronics engineering from the Southern Taiwan University of Technology, Tainan, Taiwan, in 2000, the M.S. degree in computer science and information engineering from the National Taipei University of Technology, Taipei, Taiwan, in 2003, and the Ph.D. degree in electrical engineering from National Cheng Kung University, Taiwan, in 2008. He is currently an Assistant Professor with the Electronic Engineering Department, Southern Taiwan University of Science and Technology. His research interests include the areas of cloud/IOT systems and applications, protocols for heterogeneous networks, and WSN/high-speed networks design and analysis.

JING-JOU TANG was born in Tainan, Taiwan, in 1964. He received the B.S., M.S., and Ph.D. degrees from National Cheng Kung University, Taiwan, in 1986, 1988, and 1995, respectively, all in electrical engineering. From 1988 to 1990, he was an Engineering Ensign in the Taiwan Navy. From 1990 to 1992, he was a CAE Engineer with Mitac International Corporation. In 1995, he was a member of the Artificial Heart Research Center with Tainan Municipal Hospital. He joined as a Faculty Member with the Southern Taiwan University of Science and Technology, Tainan, in 1995, where he is currently a Professor with the Department of Electronic Engineering. His main research area is VLSI testing and testable design.

KATHERINE SHU-MIN LI (S’04–M’06–SM’13) received the B.S. degree from Rutgers University, New Brunswick, NJ, and the M.S. and Ph.D. degrees from National Chiao Tung University, Hsinchu, Taiwan, in 2001 and 2006, respectively. She is currently a Full Professor with the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan. Her current research interests include interposer test, 2.5-D/3-D/SiP IC test, microfluidic chip synthesis and test, hardware Trojan, side channel effect, crosstalk effects, signal integrity, SOC testing, floorplanning and routing for testability and yield enhancement, design for yield, scan reordering, scan routing, lowpower scan techniques, particularly on oscillation ring test schemes, and interconnect optimization. He is a Senior Member of the IEEE Circuits and Systems Society and the IEEE Women in Engineering, and a member of the Association for Computing Machinery and the ACM Special Interest Group on Design Automation.

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