研究項目
Research
浸大人工智能升級技術獲「安心出行」採用
HKBU designs an AI-enabled tracing module for LeaveHomeSafe
由浸大徐建良教授領導的團隊開發的保障私隱人工智能升級技術，獲政府採用於新冠病毒風險通知流動應用程式「安心出行」。採用升級技術後，「安心出行」現在可運用嶄新的人工智能演算法，配合手機內置的加速計，監測用戶的活動模式，在毋須點擊手機屏幕的情況下，自動記錄用戶離開的士的時間。按此瀏覽更多。
The artificial intelligence (AI)-enhanced technology developed by Professor Xu Jianliang and his team has been incorporated into the LeaveHomeSafe, a contact tracing mobile app launched by the Government. Powered by a novel AI algorithm, LeaveHomeSafe can now utilise the built-in accelerometer in mobile phones to automatically record the time when users get out of a taxi. Click here to view more.
香港新冠復康計劃
Hong Kong Rehabilitation Program For Covid-19
浸大系統健康研究實驗室推出「香港新冠復康計劃」，為癒後新冠肺炎患者建立一個揉合中草藥應用的遙距復康訓練方法，幫助冠肺炎患者減輕身體及認知方面的困難與影響。按此瀏覽更多。
HKBU’s System Health Lab launched the “Hong Kong Rehabilitation Program For Covid-19” to establish an online tele-exercise rehabilitation approach in combination with Chinese herbal medicine for patients recovering from COVID-19, helping them to overcome their physical and psychological symptoms. Click here to view more.
同心抗疫 戰勝疫情
Combating COVID-19 in Hong Kong
來自香港浸會大學傳播學、計算機科學、公共衞生及中醫領域的學者，組成跨學科團隊，研究如何識別和解決香港抗疫策略可能存在的盲點，採取新的舉措和策略，有效地應對疫情。團隊將從如何建立社區復原力，增強社會信任與共情，減少焦慮和恐慌等方面開展研究。按此瀏覽更多。
Three teams of interdisciplinary scholars from Communication, Computer Science, Public Health, and Chinese Medicine at HKBU call for new initiatives and strategies to cope with the surge by identifying and addressing possible blind spots in the city’s coping strategies. The research teams focus on building up community resilience, enhancing social trust and empathy, and reducing anxiety and panic. Click here to view more.
貝葉斯數據同化方法應用於新冠肺炎
Bayesian data assimilation approach for COVID-19 applications
浸大和英國倫敦帝國學院的研究人員合作開發一種新的計算方法，以貝葉斯數據同化方法為基礎，估算計算流行病學中的傳播動力學。研究團隊所提出的方法，對流行病研究和干預措施的政策設計與監督具有重大意義。該研究發現已於刊載於頂尖期刊《Plos Computation Biology》，並且系統已開源供研究者共享使用。按此閱讀論文。
Researchers from HKBU and Imperial College London work together to develop a new computational approaches for estimating transmission dynamics in computational epidemiology based on Bayesian data assimilation. The proposed methods have shown its significance in epidemic research and intervention policy design and monitoring. The research founding was published in the top-tier journal Plos Computation Biology sharing the open source system for public use. Click here to view the paper.
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