This page has only limited features, please log in for full access.

Unclaimed
Zhidong Su
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
Profile ImageJames Su National Applied Research La...
Following: 1 user
View all

Feed

Journal article
Published: 23 March 2021 in IEEE Robotics and Automation Letters
Reads 0
Downloads 0

Negative affects such as anger, fear, nervousness, depression, etc., may increase human's susceptibility to illness. In this paper, we propose a negative emotion management system that is able to recognize negative emotions through ECG signal and perform emotion regulation through a robot assistant, which has a potential for reducing health risks. A smart shirt is developed to collect the ECG signal from the human body. The robot assistant has the ability to engage in verbal conversations with humans. Recurrence Quantitative Analysis (RQA) is used to extract ECG features for emotion classification purpose. Along with our own dataset, two other public datasets, RECOLA and DECAF, are also used to evaluate our methodology. The detection of negative emotion can trigger the robot assistant to help the user get out of such situations through interactive conversations. We tested and evaluated the proposed framework through experiments. We also assessed the effectiveness of the interactions with the robot on the emotional well-being of older adults.

ACS Style

Minh Pham; Ha Manh Do; Zhidong Su; Alex J. Bishop; Weihua Sheng. Negative Emotion Management Using a Smart Shirt and a Robot Assistant. IEEE Robotics and Automation Letters 2021, 6, 4040 -4047.

AMA Style

Minh Pham, Ha Manh Do, Zhidong Su, Alex J. Bishop, Weihua Sheng. Negative Emotion Management Using a Smart Shirt and a Robot Assistant. IEEE Robotics and Automation Letters. 2021; 6 (2):4040-4047.

Chicago/Turabian Style

Minh Pham; Ha Manh Do; Zhidong Su; Alex J. Bishop; Weihua Sheng. 2021. "Negative Emotion Management Using a Smart Shirt and a Robot Assistant." IEEE Robotics and Automation Letters 6, no. 2: 4040-4047.

Journal article
Published: 24 February 2021 in IEEE Robotics and Automation Letters
Reads 0
Downloads 0

Memory loss is a part of normative aging. Many older adults commonly forget to take prescribed medication, which can have an adverse effect on health. Therefore, it is important to provide older adults with a medication reminder service. There are several mobile apps and devices capable of reminding medication, but their user interfaces and operations are usually unfriendly to seniors. Considering the above demands and shortcomings, we proposed a conversation-based medication management system (CMMS). The CMMS uses a companion robot and the cloud to create medication reminders and check medication adherence. We implemented the CMMS in our ASCC (Advanced Sensing, Computation and Control) companion robot. To evaluate the CMMS, we tested our system through the mobile app end with 23 human subjects and the robot end with 15 human subjects. The feedback from the post-test survey shows that the convenience, usefulness and total rating of our system is 8.217, 8.696 and 8.478 out of 10 from the mobile app end and 9.000, 8.933 and 8.533 out of 10 from the robot end, respectively. The System Usability Scale (SUS) score of our system is 81.333 from the robot end users, which means the participants had a high satisfaction level when using the system.

ACS Style

Zhidong Su; Fei Liang; Ha Manh Do; Alex Bishop; Barbara Carlson; Weihua Sheng. Conversation-Based Medication Management System for Older Adults Using a Companion Robot and Cloud. IEEE Robotics and Automation Letters 2021, 6, 2698 -2705.

AMA Style

Zhidong Su, Fei Liang, Ha Manh Do, Alex Bishop, Barbara Carlson, Weihua Sheng. Conversation-Based Medication Management System for Older Adults Using a Companion Robot and Cloud. IEEE Robotics and Automation Letters. 2021; 6 (2):2698-2705.

Chicago/Turabian Style

Zhidong Su; Fei Liang; Ha Manh Do; Alex Bishop; Barbara Carlson; Weihua Sheng. 2021. "Conversation-Based Medication Management System for Older Adults Using a Companion Robot and Cloud." IEEE Robotics and Automation Letters 6, no. 2: 2698-2705.

Journal article
Published: 01 January 2020 in IEEE Access
Reads 0
Downloads 0

As the problem of an aging population becomes more and more serious, social robots have an increasingly significant influence on human life. By employing regular question-and-answer conversations or wearable devices, some social robotics products can establish personal health archives. But those robots are unable to collect diet information automatically through robot vision or audition. A healthy diet can reduce a person’s risk of developing cancer, diabetes, heart disease, and other age-related diseases. In order to automatically perceive the dietary composition of the elderly by listening to people’s chatting, this paper proposed a chat-based automatic dietary composition perception algorithm (DCPA). DCPA uses social robot audition to understand the semantic information and percept dietary composition for Mandarin Chinese. Firstly, based on the Mel-frequency cepstrum coefficient and convolutional neural network, a speaker recognition method is designed to identify speech data. Based on speech segmentation and speaker recognition algorithm, an audio segment classification method is proposed to distinguish different speakers, store their identity information and the sequence of expression in a speech conversation. Secondly, a dietetic lexicon is established, and two kinds of dietary composition semantic understanding algorithms are proposed to understand the eating semantics and sensor dietary composition information. To evaluate the performance of the proposed DCPA algorithm, we implemented the proposed DCPA in our social robot platform. Then we established two categories of test datasets relating to a one-person and a multi-person chat. The test results show that DCPA is capable of understanding users’ dietary compositions, with an F1 score of 0.9505, 0.8940 and 0.8768 for one-person talking, a two-person chat and a three-person chat, respectively. DCPA has good robustness for obtaining dietary information.

ACS Style

Zhidong Su; Yang Li; Guanci Yang. Dietary Composition Perception Algorithm Using Social Robot Audition for Mandarin Chinese. IEEE Access 2020, 8, 8768 -8782.

AMA Style

Zhidong Su, Yang Li, Guanci Yang. Dietary Composition Perception Algorithm Using Social Robot Audition for Mandarin Chinese. IEEE Access. 2020; 8 (99):8768-8782.

Chicago/Turabian Style

Zhidong Su; Yang Li; Guanci Yang. 2020. "Dietary Composition Perception Algorithm Using Social Robot Audition for Mandarin Chinese." IEEE Access 8, no. 99: 8768-8782.

Journal article
Published: 14 January 2019 in Remote Sensing
Reads 0
Downloads 0

In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment.

ACS Style

Guanci Yang; Zhanjie Chen; Yang Li; Zhidong Su. Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm. Remote Sensing 2019, 11, 149 .

AMA Style

Guanci Yang, Zhanjie Chen, Yang Li, Zhidong Su. Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm. Remote Sensing. 2019; 11 (2):149.

Chicago/Turabian Style

Guanci Yang; Zhanjie Chen; Yang Li; Zhidong Su. 2019. "Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm." Remote Sensing 11, no. 2: 149.

Conference paper
Published: 01 July 2018 in 2018 13th World Congress on Intelligent Control and Automation (WCICA)
Reads 0
Downloads 0

Different smart products produced by various manufacturers work on dissimilar communication standards incompatible with one other. Such incompatibility renders communication of products challenging and serve as a severe hindrance to the development of the smart home industry. Considering the issue and advantage of the social robot, this research investigates a Home Assistant-based collaborative framework of multi-sensor fusion for the smart home social robot. We review the related work and briefly introduce the Home Assistant platform. Then, we detail the system design and its implementation. To check system performance, we designed two test cases with different scenarios. Finally, we detail the test results, which indicates that the system can fuse the multi-sensors and performs satisfactorily according to the designed control logic.

ACS Style

Yang Li; Weihua Sheng; Guanci Yang; Baojuan Liang; Zhidong Su; Zhanjie Chen. Home Assistant-Based Collaborative Framework of Multi-Sensor Fusion for Social Robot*. 2018 13th World Congress on Intelligent Control and Automation (WCICA) 2018, 401 -406.

AMA Style

Yang Li, Weihua Sheng, Guanci Yang, Baojuan Liang, Zhidong Su, Zhanjie Chen. Home Assistant-Based Collaborative Framework of Multi-Sensor Fusion for Social Robot*. 2018 13th World Congress on Intelligent Control and Automation (WCICA). 2018; ():401-406.

Chicago/Turabian Style

Yang Li; Weihua Sheng; Guanci Yang; Baojuan Liang; Zhidong Su; Zhanjie Chen. 2018. "Home Assistant-Based Collaborative Framework of Multi-Sensor Fusion for Social Robot*." 2018 13th World Congress on Intelligent Control and Automation (WCICA) , no. : 401-406.

Conference paper
Published: 01 July 2018 in 2018 13th World Congress on Intelligent Control and Automation (WCICA)
Reads 0
Downloads 0

Simultaneous localization and mapping (SLAM) technology is adopted for localization and environmental map reconstruction in unknown environments. Visual SLAM provides rich three-dimensional map information that helps social robots complete complex tasks. Visual odometry (VO) is an important component of Visual SLAM. Here, we first review the principles of the feature and direct methods of VO. Thereafter, we introduce a social robot platform for testing the two VO methods. We detail the implementation and setup of the feature and direct methods of VO. Finally, we compare and analyze the two methods and provide suggestions for their applications.

ACS Style

Zhanjie Chen; Weihua Sheng; Guanci Yang; Zhidong Su; Baojuan Liang. Comparison and Analysis of Feature Method and Direct Method in Visual SLAM Technology for Social Robots*. 2018 13th World Congress on Intelligent Control and Automation (WCICA) 2018, 413 -417.

AMA Style

Zhanjie Chen, Weihua Sheng, Guanci Yang, Zhidong Su, Baojuan Liang. Comparison and Analysis of Feature Method and Direct Method in Visual SLAM Technology for Social Robots*. 2018 13th World Congress on Intelligent Control and Automation (WCICA). 2018; ():413-417.

Chicago/Turabian Style

Zhanjie Chen; Weihua Sheng; Guanci Yang; Zhidong Su; Baojuan Liang. 2018. "Comparison and Analysis of Feature Method and Direct Method in Visual SLAM Technology for Social Robots*." 2018 13th World Congress on Intelligent Control and Automation (WCICA) , no. : 413-417.