Development and Applications of Daily-use Fall Risk Assessment Device to Prevent Elderly People From Falling

This research attempts to develop an artificial intelligence (AI) enabled device for measuring the dynamic balance ability of older people with a sensor using an optical principle called Frustrated Total Internal Reflection. The AI-based algorithm embedded in the device performs the data analysis for balance ability assessment and falling risk prediction. As a critical part of the research, a large-scale user study is needed to test the validity of the device regarding the dynamic balance ability assessment and the accuracy of the falling risk prediction provided by the device. Also, we plan to study the factors influencing user engagement in this device through the questionnaire-based survey and interview.

Inclusion Criteria for reference group participants:Age ≥ 60Ability to communicate with experiment operators (in Chinese or English)Ability to provide informed consent to participate after being given information about the experiment and other information that the participant must know to participateExclusion Criteria for reference group participants:• Do not fall in the past twelve monthsInclusion Criteria for intervention group participants:Age ≥ 60Ability to communicate with experiment operators (in Chinese or English)Ability to provide informed consent to participate after being given information about the experiment and other information that the participant must know to participateExclusion Criteria for intervention group participants:Inability to give written informed consent (e.g., illiterate or with cognitive impairment)Inability to stand still without any assistance for 30 seconds