Human AI symbiosis: The role of artificial intelligence in stratifying high-risk outpatient senior citizen fall events in a non-connected environments

Publication Date


Document Type

Conference Proceeding

Publication Title

Intelligent Human Systems Integration 2020: Proceedings of the 3rd International Conference on Intelligent Human Systems Integration (IHSI 2020): Integrating People and Intelligent Systems, February 19-21, 2020, Modena, Italy


Tareq Ahram, Waldemar Karwowski, Alberto Vergnano, Francesco Leali, Redha Taiar



First Page


Last Page



Senior Citizen Falls are debilitating and harmful events. Not only does it negatively affect morality, psychology, self-esteem but also tends to be very repetitive and life costly. To prevent future falls, the outpatient senior citizen needs to be equipped with real-time monitoring sensors such as, a wrist band or a sensor necklace. Nonetheless, In the world where real-time sensor monitoring systems are not available due to connectivity limitations and economic affordability, the onus of senior citizen fall predicting, and preventing, needs to be on cognitive systems that are democratized in nature and yield learning from population health analysis. In this paper, we apply population collaborative filtering techniques and artificial intelligent models to cohort high risk senior citizen clusters and alert healthcare professionals and primary care family members.


Fall event, K-Means, Kalman, Machine learning, Outpatient, Sanjeevani electronic health records, Senior Citizens


Computer Engineering