Yuanyuan Liu

Prof. Dr. Yuanyuan Liu

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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13 October 2022 in Sensors
Verified
Journal Article
Open access
PDF file
WSWenbin Sun
Wenbin Sun
ZGZilong Guo
Zilong Guo
ZYZhiqiang Yang
Zhiqiang Yang
YWYizhou Wu
Yizhou Wu
WLWeixia LAN
Weixia LAN
Yuanyuan LiuYuanyuan Liu
Yuanyuan Liu
In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

1 December 2021 in Colloids and Surfaces B: Biointerfaces
Verified
Journal Article
Closed access
CGChuang Gao
Chuang Gao
CLChunxiang Lu
Chunxiang Lu
ZJZhian Jian
Zhian Jian
TZTingrui Zhang
Tingrui Zhang
ZCZhongjian Chen
Zhongjian Chen
Yuanyuan LiuYuanyuan Liu
Yuanyuan Liu

2 January 2025 in Theranostics
Verified
Journal Article
Open access
PDF file
HLHuazhen Liu
Huazhen Liu
WSWenbin Sun
Wenbin Sun
WCWeihuang Cai
Weihuang Cai
KLKaidi Luo
Kaidi Luo
CLChunxiang Lu
Chunxiang Lu
Yuanyuan LiuYuanyuan Liu
Yuanyuan Liu
Skin injuries caused by physical, pathological, and chemical factors not only compromise appearance and barrier function but can also lead to life-threatening microbial infections, posing significant challenges for patients and healthcare systems. Artificial intelligence (AI) technology has demonstrated substantial advantages in processing and analyzing image information. Recently, AI-based methods and algorithms, including machine learning, deep learning, and neural networks, have been extensively explored in wound care and research, providing effective clinical decision support for wound diagnosis, treatment, prognosis, and rehabilitation. However, challenges remain in achieving a closed-loop care system for the comprehensive application of AI in wound management, encompassing wound diagnosis, monitoring, and treatment. This review comprehensively summarizes recent advancements in AI applications in wound repair. Specifically, it discusses AI's role in injury type classification, wound measurement (including area and depth), wound tissue type classification, wound monitoring and prediction, and personalized treatment. Additionally, the review addresses the challenges and limitations AI faces in wound management. Finally, recommendations for the application of AI in wound repair are proposed, along with an outlook on future research directions, aiming to provide scientific evidence and technological support for further advancements in AI-driven wound repair theranostics.

16 September 2016 in ACS Applied Materials & Interfaces
Verified
Journal Article
Closed access
SLShuai Li
Shuai Li
Yuanyuan LiuYuanyuan Liu
Yuanyuan Liu
LLLi-Jun Liu
Li-Jun Liu
QHQingxi Hu
Qingxi Hu

1 May 2014 in Experimental Cell Research
Verified
Journal Article
Closed access
HCHaiping Chen
Haiping Chen
Yuanyuan LiuYuanyuan Liu
Yuanyuan Liu
ZJZhenglong Jiang
Zhenglong Jiang
WCWeihua Chen
Weihua Chen
YYYongzhe Yu
Yongzhe Yu
QHQingxi Hu
Qingxi Hu

15 May 2024 in Biofabrication
Verified
Journal Article
Open access
FWFuxiao Wang
Fuxiao Wang
PSPeiran Song
Peiran Song
JWJian Wang
Jian Wang
SWSicheng Wang
Sicheng Wang
Yuanyuan LiuYuanyuan Liu
Yuanyuan Liu
JSJiacan Su
Jiacan Su
Organoids have emerged as crucial platforms in tissue engineering and regenerative medicine but confront challenges in faithfully mimicking native tissue structures and functions. Bioprinting technologies offer a significant advancement, especially when combined with organoid bioinks-engineered formulations designed to encapsulate both the architectural and functional elements of specific tissues. This review provides a rigorous, focused examination of the evolution and impact of organoid bioprinting. It emphasizes the role of organoid bioinks that integrate key cellular components and microenvironmental cues to more accurately replicate native tissue complexity. Furthermore, this review anticipates a transformative landscape invigorated by the integration of artificial intelligence with bioprinting techniques. Such fusion promises to refine organoid bioink formulations and optimize bioprinting parameters, thus catalyzing unprecedented advancements in regenerative medicine. In summary, this review accentuates the pivotal role and transformative potential of organoid bioinks and bioprinting in advancing regenerative therapies, deepening our understanding of organ development, and clarifying disease mechanisms.