Gerontechnology solution
This project aims to develop an effective solution to help elderly move aroun freely, saftely and with dignity, by analysing daily movement, and providing assisted regular training, preventive warnings and intelligent detection of falling incidents, using remote sensing and wearable sensors (in collaboration with business partners), combined with an in-house developed Keep Ai processing algorithm.
Our proposed solution provides assistance in the following areas: analysing movement and posture to detect musculoskeletal disorders and ergonomic dysfunction; assisting in training and rehabilitation; and monitoring movement and providing early warning of risky situations and emergency alerts.
Also, through partnering with some on-body monitoring gadget companies, it can provide functional features to automatically detect emergency conditions, including falls, unresponsiveness, hypothermia, heat stroke, stroke and heart attack, and send an alert to a cloud server. Through machine learning and data collection, in partnership with doctors and therapist centres, our Keep Ai system will initially go through a series of training and testing to capture clinical diagnosis data. Therefore, our solution will enable full autonomous real time emergency situation classification and decision support.
Mr Yau Siu-long* (Alumnus, Department of Computer Science, City University of Hong Kong)
Mr Lai Fung-shing (Cardiff University)
Mr Christopher Johann Friedrich Ruehl (FH Darmstadt, Germany)
Mr Chan Ka-ho (Queensland University of Technology)
* Person-in-charge
(Info based on the team's application form)
- CityU HK Tech 300 Seed Fund (2023)