Issue 1
![VibrantFENG2050500](/feng/-/media/department/feng/breadcrumb-image/vibrantfeng2050500.jpg?bc=ffffff&h=250&w=1025&hash=C1D1F0C929EB6E98EB89B7595EA9EB1B)
Vibrant@FENG - Issue 1 (May 2021)
Department of Biomedical Engineering (BME)
2 BME medical innovations have won “Global Innovation Awards” at the TechConnect Business Virtual Summit and Showcase 2020
![20201119_GIA_2](/feng/-/media/department/feng/media-release/news/2020/20201119_gia_2.jpg?la=en&hash=37B2868FF7BFF25F2D36D62DAC8929BA)
The Department of Biomedical Engineering (BME) continues to develop the existing research strength with a significant societal impact which can deliver benefit for the wider community in the areas of health and medical care.
Recently two BME medical innovations have won “Global Innovation Awards” at the TechConnect Business Virtual Summit and Showcase 2020. The PolyUStimulator co-developed by Professor Yong-ping Zheng and Dr Monzurul Alam is the first battery-free device that can achieve the same neurostimulation outcomes as a conventional electrically-powered one; an AI-based risk assessment system for knee osteoarthritis, developed by Mr Toby Li and Mr Justin Chan, MPhil students under the supervision of Dr Chunyi Wen, can aid clinical practitioners in creating personalised treatment plans. These advances in research have great potential to benefit patients.
To learn more:
https://www.polyu.edu.hk/feng/news-and-events/news/2020/20201119-polyu-wins-three-techconnect-global-innovation-awards/
Smart elderly care with computation imaging analysis
Osteoarthritis and dementia are the most common forms of non-communicable diseases in elderlies. Osteoarthritis causes pain and restricts mobility of elderly. Osteoarthritis has been a great economic burden to patients and diminish their quality of life. Diagnosis and prediction of progression have provided vital information on the decision of treatment and therapy plan. Deep learning techniques have been widely applied in different fields of imaging study. Dr. Chunyi WEN and Dr. James CHEUNG teamed up using the state of art deep learning techniques to handle the issue. Deep learning techniques have been applied for segmentation of the affected regions in knee joint and joint space. Instead of using standard single joint space (JSW) width for diagnosis and prediction, multiple joint JSWs with equal space are automatically devised from the segmented images. The multiple JSWs outperform the commonly used minimum JSW in KL-grade classification and in disease progression prediction. As multiple JSWs are more accurate and convenience to be conducted by the algorithm, it shows its potential in health care sector. On the other hand, elderly with dementia tends to loss sense of space and time, causing wandering on street. Most of elderlies with history of wandering and problem of self-caring will be arranged to live in hostel. However, a great deal of elderly with dementia are living in hostels with different physical and electronic restricts to prevent wandering. But these approaches apparently come with various ethnical problems. Remote sensing technique, such as near infrared deep camera and ultra wide band radar have been widely used for monitoring people activities and vital signs. Prof. Yongping ZHENG and Dr. James CHEUNG teamed up using near infrared deep camera and ultra wide band radar to develop the eNightLog system, which aims to overcome these issues. It provides a great protection for elderly while maximising their freedom in hostel. It also has outperformed standard electronic restricts such as pressure mat and infrared fence as shown in recent study.
Territory-wide STEM competition “Love.Innovate for Happy Ageing “ to be held on 9th July 2021
![BME2S03/S Reducing the Scientific Divide in Secondary Students through STEM](/feng/-/media/department/feng/content/enews/1/dn-reducing-the-scientific-divide-in-secondary-students-through-stem.jpg?la=en&hash=5FE7D79FE476F2646793E73BDEC8D15D)
Designed to nurture PolyU students' scientific thinking and scientific literacy by exposing them to the changing needs of the community, “Reducing the Scientific Divide in Secondary Students through STEM” is one of the Service-Learning subjects of BME.
The theme of this year is “STEM for Smart Ageing during COVID-19”. The focus of the service-learning activities will be to cultivate a spirit of STEM (science, technology, engineering and mathematics) literacy and engineering design thinking in secondary students related to technology and human health. PolyU students will be involved in the learning, teaching and designing of simple mechatronic devices to illustrate key STEM concepts to secondary students; guide and facilitate secondary students to build an assistive device to promote a healthy lifestyle. The exact nature of the project may vary depending on the needs of the collaborating schools and students. In 2020-21, more than 280 PolyU students will join BME2S03/S to provide STEM workshops to over 840 secondary students. This subject is widely recognized and strongly supported by the local secondary schools.
![Love.Innovate for Happy Ageing](/feng/-/media/department/feng/content/enews/1/dn-love-innovate-for-happy-ageing.jpg?la=en&hash=53CB170B269AB28C6454A8C060F06600)
To develop students’ innovative technologies and solutions to meet the emerging needs of the society due to the ageing population in Hong Kong, there will be a territory-wide STEM competition “Love.Innovate for Happy Ageing “ on 9th July 2021 for students to showcase their product design. Students are encouraged to use engineering design thinking to understand the advantages and disadvantages of existing assistive technologies, pay attention to the needs of end users, and suggest a more appropriate product design.
(Link: http://ymhk.org.hk/eventDetail.php?id=11)
We continue to review and refine the subject in offer and to design new projects to strengthen the contents. “Reducing the Scientific Divide in Primary and Secondary Students through STEM Projects”, is an expansion of service beneficiaries from secondary students to both primary and secondary students.