4057

Sensors and Materials, Vol. 33, No. 11 (2021) 4057–4068
MYU Tokyo
S & M 2745

Interactive Sound Generation to Aid Visually Impaired People
via Object Detection Using Touch Screen Sensor
Tias Kurniati,1 Chuan-Kai Yang,2 Tzer-Shyong Chen,3*
Yu-Fang Chung,4 Yu-Min Huang,1 and Chih-Cheng Chen5**
1Department of Statistics, Tunghai University,

No. 1727, Section 4, Taiwan Blvd., Xitun District, Taichung City 407224, Taiwan

2Department of Information Management, National Taiwan University of Science and Technology,

No. 43, Section 4, Keelung Rd., Da’an District, Taipei City 106, Taiwan
3Department of Information Management, Tunghai University,
No. 1727, Section 4, Taiwan Blvd., Xitun District, Taichung City 407224, Taiwan
4Department of Electrical Engineering, Tunghai University,
No. 1727, Section 4, Taiwan Blvd., Xitun District, Taichung City 407224, Taiwan
5Department of Automatic Control Engineering, Feng Chia University,
No. 100, Wenhua Rd, Xitun District, Taichung City 407, Taiwan(Received July 1, 2021; accepted October 25, 2021)

Keywords: sound generation, touch sensor, object detection, image processing

Along with the invention of portable devices, such as smartphones and tablets, computerbased touch screen sensor-assistive technologies have become significantly more affordable than
traditional tactile graphics. The sensor panel in these technologies allows users to receive visualand auditorial responses via interaction with the device. However, visually impaired individuals(with a lack or loss of ability to see) will not find visual responses useful when using tablets or
smartphones. Therefore, in this paper we propose a system that helps visually impaired people
comprehend information on electronic devices with the help of auditory action feedback. We
develop a multimedia system for sound production from a given image via object detection. In
this study, YOLO (You Only Look Once) is used in object detection for sonification. A pretrained model is used; thus, a wider range of object classification can be identified. The systemgenerates the corresponding sound when an object on the sensor screen is touched. The purposeof our research is to aid visually impaired people to perceive information of a picture shown on
the device by touching the detected object. The device was tested by simulating visually
impaired people by blindfolding people with normal vision, who filled out questionnaires on its
performance. The results indicate that most of the users found that the sound presented by thedevice was helpful for telling them what the shown image was.

1.

Introduction

All our understanding of the world is built upon our ability to process information throughthe five senses. All of these senses are important; however, hearing is one of the most essential*Corresponding author: e-mail: arden@thu.edu.tw**Corresponding author: e-mail: chenccheng@fcu.edu.twhttps://doi.org/10.18494/SAM.2021.3528

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

4058

Sensors and Materials, Vol. 33, No. 11 (2021)

and fundamental senses in obtaining information about the world. Sound is well known for its
important role in the way we perceive and interact with the environment. Regarding vision, theWorld Health Organization (WHO) estimates that there are 285 million visually impaired people
in the world, out of which 39 million are blind.(1) Two common ways of presenting information
to help visually impaired people are tactile graphics and sonification. However, these days
people use electronic devices and screens instead of printed braille to access text and pictures,
especially the younger generation.
In this paper, we propose a multimedia system for sound production from a given image
based on object detection utilizing the You Only Look Once (YOLO) deep learning method.
Firstly, a random image is uploaded into the system. Immediately after it appears on the touch
screen sensor, users can hear a sound associated with the image by touching the object. A pretrained model is used in the system, so a more extensive range of objects can be recognized.
Once an image is uploaded to the system, it is run through a single convolutional network to
detect multiple bounding boxes and class probabilities, then weights are used to optimize thepredicted bounding boxes and send back the picture with a bounding box to the system. This
technology helps visually impaired people perceive information of a picture shown on the sensor
device with the use of touch. For example, Fig. 1 illustrates the difference in how the system
handles pictures of an object with and without an associated sound. The sound of a cat (meow) is
generated when the user touches the cat shown on the sensor screen. Meanwhile, for the chair, asilent object, the system pronounces the word of the object automatically and the word “chair” isspoken by the system. A user study showed that more than 90% of users can recognize objects
correctly.
This paper is organized as follows. In Sect. 2, we explain some of the previous related works,
while in Sect. 3, we discuss the system methodology. In Sec. 4, we present the experimental
results of the system. Finally, in Sect. 5, we conclude the paper and discuss some potential future
works.

(a)(b)

Fig. 1.

(Color online) (a) Picture of an object with a sound. (b) Picture of an object without a sound.

Sensors and Materials, Vol. 33, No. 11 (2021)

2.

4059

Related Works

Object detection has been a popular area of research over the past few decades, as indicatedby the large number of new applications related to identifying objects based on visual detection,such as facial expression recognition,(2) navigation assistants,(3) autonomous robot navigation,(4)self-driving systems,(5) image recognition,(6) and pedestrian detection.(7)Many approaches have been used to assist visually impaired people in obtaining information
on a digital platform. A software package called PLUMB for a tablet PC was developed by
Cohen et al.(8) They used audio feedback and a pen-based interface to relay information ongraphs from the start vertex to the finish vertex. The authors used the variations of the loudness
and vibration intensity based on the HSB color model in the elements of the graphs. Hence, thesystem was unable to guarantee real-time audio feedback, which caused discomfiture in users
when the graph information was conveyed.
Wörtwein et al.(9) proposed image sonification through a mobile, interactive, and web-based
approach. To evaluate the approach, visually impaired users were given three tasks to complete:mathematical graph identification, proportion estimation in bar charts, and pathfinding on floor
maps. However, there were some drawbacks related to the system. The implemented system
could not guarantee real-time reactions, which sometimes irritated users trying to identify theshapes and details of an image through mouse/finger movement.
Cavaco et al.(10) developed a software tool that assisted visually impaired people inidentifying the color and luminosity of an image through image sonification. The software tool
extracted the color information of an image or video by extracting HSV (hue, saturation, value)
information, which was converted into the audio attributes of pitch, timbre, and loudness,
respectively. Nonetheless, the audio generated only seven colors within one musical octave,
which was insufficient for users to differentiate the sounds for different colors. This resulted inan overall correct answer rate of only 60% for two subjects who were born blind and 48.33% for
the remaining subjects.
More sonification-based studies were presented by Yoshida et al.(11) and Krishnan et al. (6)They proposed a framework to assist visually impaired users in recognizing an object in animage according to image edge features and distance-to-edge maps by transforming basic object
shapes into sounds. The system was implemented on the touch screen of a mobile device,
allowing users to explore the image content by moving their fingers over the screen.
A multilevel approach to the sonification of images was also developed in 2013 by Banf andBlamz.(12) They presented a system to help visually impaired people obtain direct perceptual
access to images via acoustic signals. Users explored an image actively on a touch screen thenreceived auditory feedback about the image content at the current position. In the system, lowlevel information (color, edges, and roughness) was combined with mid-level and high-level
information from machine learning algorithms. For object recognition, the OpenCV library was
employed, which enabled us to implement a Bag of Visual Words as well as perform support
vector machine (SVM) detection and localization algorithms. Then, both algorithms weretrained on the 20 object classes provided by the Visual Object Classes Challenge 2008
(VOC2008). The experimental results indicated that the system was useful and could help

4060

Sensors and Materials, Vol. 33, No. 11 (2021)

visually impaired people access the content of an image. However, the auditory feedback was
limited to an acoustic rhythm such as a drum, bass, or noise, whereas a wider range of acoustic
feedback is desirable.
The most recent research related to the use of deep learning was presented by Saldana andMendizabal-Ruiz.(13) They synthesized sound from a geometrical image using a method basedon a convolutional neural network (CNN). The process started from a deep learning networkthat learned how to associate a pattern in an image with a sound, then some parameters were set
to generate the sound. For each modification of the image pattern, the system produced adifferent sound.
Cardillo et al. proposed an electromagnetic sensor to assist visually impaired people becomeaware of obstacles surrounding them in a range wider than that provided by a traditional cane by
mounting a microwave radar on a traditional cane.(14) Patil et al. presented a NavGuide system,
which was implemented in shoes, to help visually impaired people navigate outdoors by
informing the user of the surrounding environment.(15) The feedback utilized vibration andaudio feedback mechanisms. These two works mainly focused on helping visually impaired
people to navigate.
In many of these previous studies, systems were developed to assist visually impaired people
in different ways. In contrast, the purpose of our system is to help visually impaired people
identify objects, especially when they are presented as pictures on smartphones or tablets.
Therefore, an image sonification platform that can produce the corresponding sound feedback ofan object detected is needed to assist visually impaired people in retrieving information.

3.

System Overview

The system architecture is illustrated in Fig. 2. Object detection in our system is based on the
YOLO framework. We developed the system under the webserver node.js associated with
YOLO. As shown in Fig. 2, before the image is processed by YOLO, it is uploaded and sent tonode.js to be stored permanently. Then, the image is sent to the YOLO system to run boundingbox prediction, class prediction, prediction across scales, and feature extraction, resulting in
multiple bounding boxes and class probabilities. Then, weights are used to maximize thepredicted bounding boxes and confidence, then the final detection of the region and class isperformed. The resulting image with bounding boxes is then sent back to the interface. The

Fig. 2.

(Color online) System architecture of object detection sonification.

Sensors and Materials, Vol. 33, No. 11 (2021)

4061

sound corresponding to the object is played when the object on the screen sensor panel istouched.
3.1

Object detection

The image classification procedures in the sonification approach are next explained in Sect.
3.1, then sound generation is discussed in Sect. 3.2.
3.1.1 Bounding box prediction
As shown in Fig. 3, an image is first divided into A × A grids, where each grid predicts thenumber of bounding boxes, B, and confidence scores for the boxes and C class probabilities.
These conﬁdence scores reﬂect how conﬁdent the model is of predicting the content of a single
box and also how accurate it is in predicting an object. Here, we define the confidence asc(object) × IOUtruthpred, where IOU refers to intersection over union. If no object exists in thecell, the conﬁdence score is zero.
Each bounding box involves five predictions, namely, x, y, w, h, and confidence, where (x, y)
represents the coordinates of the box related to the grid cell and (w, h) represents the width andheight of the whole image. Finally, the confidence score in the predictions is encoded as an A × A
× (B × 5 + C) tensor.(16)
3.1.2 Class prediction
Each grid cell predicts C class probabilities, c(Classi|Object), given by Eq. (1). These
probabilities are applied to each grid cell containing an object. Each set of class probabilities per
grid cell is predicted, regardless of B.
c(Classi |Object)c(Object) ⁎ IOUtruthpred = c(Classi) ⁎ IOUtruthpredFig. 3.

(Color online) Model of image classification.

(1)

4062

Sensors and Materials, Vol. 33, No. 11 (2021)

3.1.3 Prediction across scales
YOLO predicts boxes at three different scales: bounding box, object identification, and classprediction. The system extracts features from those scales using a similar concept to feature
pyramid networks (FPNs) as shown in Fig. 4.(17) Compared with other architectural systems, theFPN shows a significantly higher level of performance for testing images. The FPN comprises
bottom-up and top-down pathways, where the bottom-up pathway employs a CNN for feature
extraction, while the top-down pathway decreases the spatial resolution. When the higher-levelstructures of the image are detected, the semantic value (where the object is assigned to classprediction) for each layer is increased. Then, the same feature is performed one more time toobtain the final scale of the predicted boxes.
3.1.4 Feature extractor
Darknet-53, the backbone of YOLO, is used to extract features. Darknet-53 is a CNN with 53
layers, which allows the system to load a pre-trained version of the network with millions ofimage datasets. The pre-trained network classifies thousands of objects. As a result, objects canbe detected from a wide range of images. This will allow the system to help visually impaired
people detect a wide range of objects in pictures. The network uses successive 3 × 3 and 1 × 1
convolutional layers with an image input size of 256 × 256.(18) In addition, the network also adds
a residual structure, which sets up a shortcut link between several layers so that it can increase
the depth of the network without reducing its accuracy and solve the problem of gradientexplosion or disappearance that can easily occur owing to the excessive depth of the network. (19)A list of the network framework parameters of Darknet-53 is shown in Table 1.
3.2

Sound generation

The system produces sounds in two ways: by producing a suitable sound for an object and bypronouncing the word of the object. The sounds used for object detection are downloaded fromhttps://www.freesound.org and stored in a local database until called by the system. Users hear
the corresponding sound when they touch the bounding box of the detected object shown. Theinterface of the system is shown in Fig. 5.

Fig. 4.

(Color online) Feature pyramid network.

Sensors and Materials, Vol. 33, No. 11 (2021)

Table 1
Network parameters of Darknet-53.
Type
Filters
Convolutional
32
Convolutional
64
Convolutional
32
1×Convolutional
64
Residual
Convolutional
128
Convolutional
64
2× Convolutional
128
Residual
Convolutional
256
Convolutional
128
8× Convolutional
256
Residual
Convolutional
512
Convolutional
256
8× Convolutional
512
Residual
Convolutional
1024
Convolutional
512
4× Convolutional
1024
Residual
Avgpool
Connected
Softmax

Fig. 5.

4063

Size
3×3
3 × 3/2
1×1
3×3
3 × 3/2
1×1
3×3
3 × 3/2
1×1
3×3
3 × 3/2
1×1
3×3
3 × 3/2
1×1
3×3
Global1000

Output256 × 256
128 × 128

128 × 128
64 × 64

64 × 64
32 × 32

32 × 32
16 × 16

16 × 16
8×8

8×8(Color online) Interface of object detection through sonification.

As depicted in Fig. 5, each object has its own bounding box. Once the user touches any of thebounding boxes, the system calls the sound in accordance with the label name of each boundingbox. For example, when users touch the bounding box of the cat or dog, the meow of a cat or thebark of a dog is emitted by the system, and if an object without a sound is touched, the word of
the object is spoken.

4064

Sensors and Materials, Vol. 33, No. 11 (2021)

4.

Experimental Results and Discussion

4.1

Experiments

The system was developed using JavaScript and C++, both of which are compatible with
node.js. We run the system on an ASUS machine powered by an Intel Core i7-6700 Processor
with 3.40 GHz, 32 GB memory and an NVIDIA GeForce GTX-1080 Ti 11 GB graphics card. We
use Ubuntu as the operating system as it is an open source. The specifications of the PC used are
given in Table 2. The interface is viewed using Google Chrome, whose local host system istunneled into a temporary URL, so that all units of the sensor panel are accessible in real time.
4.1.1 Participants
A total of 20 blindfolded volunteers aged between 20 and 40 participated in the evaluation
and were recruited through invitation. Most of the participants were graduate students fromvarious university departments. They consisted of nine females and eleven males. All
participants had normal hearing.
4.1.2 Environment
During the user study, the participants entered the room equipped with a computer, a sensor
screen unit, and a headband to cover their eyes. Once the eyes of the participants were covered,
the researcher uploaded a random image to the system and asked the participants to hold thesensor screen unit and touch the sensor screen with their fingers. Once their fingers hovered on
the image, they heard the corresponding sound or spoken word of the image, then guessed thename of the detected object. Five images, including images containing objects with and without
a sound, were presented for each participant.
4.1.3 Questionnaire
Immediately after the experiment, participants were asked to check the correct answers.
Then, they filled in a questionnaire to evaluate the system regarding how well it detected objects.
The Likert scale was adopted to classify each evaluation as a score from 1 to 10, where 1
indicated “very inappropriate” and 10 indicated “very appropriate”. The questionnaire wasdivided into two sections. The questions in the first section asked about participants’ details
Table 2
Specifications of PC used.
Device typePC desktop (server system)
CPUIntel Core i7-6700, 3.40 GHz
Memory32 GB
GPU
NVIDIA GTX 1080 Ti 11 GB
Operating system
Ubuntu 16.04 LTS 64-bit

Touch sensor panel (interface system)
2 × 2.15 GHz + 2 × 1.6 GHz
4 GB
Qualcomm Adreno 530
Android 7.0 Nougat

Sensors and Materials, Vol. 33, No. 11 (2021)

4065

including gender, age, and hearing condition, and those in the second section asked them toevaluate the system of object detection through sonification.
4.2

Results

The overall percentage of correct answers was 98% (Fig. 6). Incorrect answers were reported
to be due to the network error and the very high resolution of some images, which resulted in along loading time and bounding boxes not appearing during the test. Users reported that when
the network was stable and the uploaded image had a normal resolution, they were able to givethe correct answer.
As we can see in Fig. 7, most of the users agreed that the sound was appropriate by giving ascore of 8 out of 10 or higher, and none of them gave a score of less than 5. This indicates thatmost participants were satisfied with the performance of the system for object detection throughsonification.

Fig. 6.

Fig. 7.

(Color online) Distribution of correct and incorrect answers.

(Color online) Users’ perceptions of object detection appropriateness.

4066

Sensors and Materials, Vol. 33, No. 11 (2021)

Most users agreed that the presented sound or the spoken word matched the object detected.
Only two users thought that the sound did not match the image. This was because during thetesting, sometimes bounding boxes were not shown, so the sound could not be heard, which
occurred when a large image with high resolution was uploaded and a longer time was required
to process the image.
4.3

Discussion

Many systems have been developed to help visually impaired people perceive information ofimages on a screen.(6,9–13) Some previous systems also implemented a deep learning
methodology;(4,13,14,19) however, none of them used deep learning to detect objects in images.
Therefore, the novelty of our system is that a deep learning method is implemented for object
detection, then the sound or word of the object is presented to the user. In the future, we will
improve the system so that it can process images with both low and high resolutions. It may also
be possible to incorporate this technology into wearable IoT devices to be implemented for therecognition of a wider range of images to help visually impaired people live independently.

5.

Conclusions

In this study, we have presented an interactive soniﬁcation platform using a touch screen
sensor to help visually impaired people access digital content on electronic devices. To evaluate
the system, we performed a user study involving blindfolded participants. Results showed that
98% of the answers given by participants were correct and matched the object detected by thesystem. Among the participants, 90% of them also agreed that our system produced theassociated sound or spoken word associated with the detected object. The proposed system is a
new medium for helping visually impaired people distinguish objects shown on the touch screenof electronic devices, such as smartphones and tablets. The system has the potential to provide
more features to assist visually impaired people. We also hope that in the future, this technology
will be incorporated into wearable devices and used to recognize a wider range of images. For
instance, the utilization of wearable devices with a small camera to detect objects could help
visually impaired people more easily live independently.

Acknowledgments
This work was partially supported by the Ministry of Science and Technology of Taiwan,
R.O.C., through Grant No. 109-2221-E-029-019.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from thecorresponding author. The data are not publicly available because further study will be carriedout using the same data.
Conflicts of Interest: The authors declare no conflicts of interest.

Sensors and Materials, Vol. 33, No. 11 (2021)

4067

References
1 World Health Organization : https://www.who.int/blindness/publications/globaldata/en/ (accessed May 2021).
2 H. Kusuma, M. Attamimi, and H. Fahrudin: Bull. Electr. Eng. Inf. 9 (2020) 1208. https://doi.org/10.11591/eei.
v9i3.2030
3 Y.-Z. Hsieh, S.-S. Lin, and F.-X. Xu: Multimed. Tools Appl. 79 (2020) 29473. https://doi.org/10.1007/s11042020-09464-7
4 M. Luong and C. Pham: J. Int. Rob. Syst. 101 (2021) 1. https://doi.org/10.1007/s10846-020-01262-5
5 D.-H. Wang, J. Li, and S. Zhu: Neurocomputing 428 (2021) 361. https://doi.org/10.1016/j.neucom.2020.02.128
6 K. G. Krishnan, C. M. Porkodi, and K. Kanimozhi: Proc. 2013 Int. Conf. Communications and Signal
Processing (IEEE, 2013) 943–946. https://doi.org/10.1109/iccsp.2013.6577195
7 P. Dollar, C. Wojek, B. Schiele, and P. Peron: IEEE Trans. Pattern Anal. Mach. Intell. 34 (2011) 743. https://doi.
org/10.1109/TPAMI.2011.155
8 R. F. Cohen, V. Haven, J. A. Lanzoni, A. Meacham, J. Skaff, and M. Wissell: Proc. 2006 ACM SIGACCESS
Conf. Computers and Accessibility (IEEE, 2006) 119–124. https://doi.org/10.1145/1168987.1169008
9 T. Wörtwein, B. Schauerte, K. Müller, and R. Stiefelhagen: Proc. 2016 Int. Conf. Computers Helping People
with Special Needs (ICCHP, 2016) 212–219. https://doi.org/10.1145/2818346.2823298
10 S. Cavaco, J. T. Henriques, M. Mengucci, N. Correia, and F. Medeiros: Proc. 2013 Int. Conf. Health and Social
Care Information Systems and Technologies (HCIST, 2013) 1048–1057. https://doi.org/10.1016/j.
protcy.2013.12.117
11 T. Yoshida, K. M. Kitani, H. Koike, S. Belongie, and K. Sclei: Proc. 2011 Augmented Human Int. Conf. (ACM,
2011) 1–4. https://doi.org/10.1145/1959826.1959837
12 M. Banf and V. Blanz: Proc. 2013 Augmented Human Int. Conf. (ACM, 2013) 162–169. https://doi.
org/10.1145/2459236.2459264
13 R. Saldana and G. Mendizabal-Ruiz: Proc. 2020 17th Int. Conf. Electrical Engineering, Computing Scienceand Automatic Control (CCE, 2020) 1–5. https://doi.org/10.1109/CCE50788.2020.9299207
14 E. Cardillo, V. D. Mattia, G. Manfredi, P. Russo, A. D. Leo, A. Caddemi, and G. Cerri: IEEE Sens. J. 18 (2018)
2568. https://doi.org/10.1109/JSEN.2018.2795046
15 K. Patil, Q. Jawadwala, and F. C. Shu: IEEE Trans. Hum.-Mach. Syst. 48 (2018) 172. https://doi.org/10.1109/
THMS.2018.2799588
16 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi: Proc. 2016 IEEE Conf. Computer Vision and Pattern
Recognition (CVPR, 2016) 779–788. https://doi.org/10.1109/CVPR.2016.91
17 T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie: Proc. 2017 IEEE Conf. Computer Vision
and Pattern Recognition (CVPR, 2017) 936–944. https://doi.org/10.1109/CVPR.2017.106
18 J. Redmon and. A. Farhadi: Technical Report YOLOv3: An Incremental Improvement (2018). https://arxiv.org/
abs/1804.02767
19 J. Cao, C. Song, S. Peng, S. Song, X. Zhang, Y. Shao, and F. Xiao: J. Sens. 20 (2020) 3646. https://doi.
org/10.3390/s20133646
20 YOLO: Real-Time Object Detection: https://pjreddie.com/darknet/yolo (accessed May 2021).

About the Authors
Tias Kurniati received her Master’s degree in information management from
National Taiwan University of Science and Technology in 2018. She iscurrently a Ph.D. student at the Department of Statistics, Tunghai University,
focusing on information management. Her main research interests are in deeplearning, image and text recognition, computer graphics, and information
security.

4068

Sensors and Materials, Vol. 33, No. 11 (2021)

Chuan-Kai Yang received his Ph.D. degree in computer science from Stony
Brook University, USA, in 2002 and his M.S. and B.S. degrees in computerscience and mathematics from National Taiwan University in 1993 and 1991,
respectively. He is currently a professor of the Information Management
Department, National Taiwan University of Science and Technology. His
research interests include computer graphics, scientific visualization,
multimedia systems, and computational geometry.
Tzer-Shyong Chen received his Ph.D. from the Department of ElectricalEngineering (Computer Science), National Taiwan University, Taiwan, in
1996. He is currently the chair of the Department of Information Management,
Tunghai University, Taiwan. He has served in an evaluation committee of theInstitute of Electrical Engineering Taiwan and is a member of IEEE. He has
authored/co-authored over 80 refereed publications. His main researchinterests are in information security, cryptography, and network security.
Yu-Fang Chung received her B.A. degree in English language, literature, andlinguistics from Providence University in 1994, her M.S. degree in computerscience from Dayeh University in 2003, and her Ph.D. degree in computerscience from National Taiwan University, Taiwan, in 2007. She is currently aprofessor of the Department of Electronic Engineering and Information
Management, Tunghai University, where she is involved in research on
information security and cryptography.
Yu-Min Huang received her Ph.D. in statistics from the University of
Minnesota-Twin Cities, USA. Currently, she is an assistant professor in the
Department of Statistics, Tunghai University. Her research interests include
time series, multivariate data analysis, network data analysis, quantitativemodeling, and high-dimensional data.

Chih-Cheng Chen is a professor at Jimei University, China. He is a senior
member of IEEE. He earned his Ph.D. degree in mechatronics engineeringfrom National Changhua University of Education. He has published 43 articles
and owns three patents. He is currently researching RFID application systems,
AIoT, machine learning, and information security.