Thermal Vision Based Fall Detection via Logical and Data driven Processes

Inadvertent falls can cause serious, and potentially fatal injuries, to at risk individuals. One such community of at-risk individuals is the elderly population where age related complications, such as osteoporosis and dementia, can further increase the incidence and negative impact of such falls. Notably, falls within that community has been identified as the leading cause of injury related preventable death, hospitalization and reduction to quality of life. In such cases, rapid detection of, and reaction, to fall events has shown to be critical to reduce the negative effects of falls within this community. Currently, a range of fall detection solutions exist, however, they have several deficiencies related to the core approach that has been adopted. This study has developed an ensemble of thermal vision-based, big data facilitated, solutions which aim to address some of these deficiencies. An evaluation of these logical and data-driven processes has occurred with the promising results presented within this manuscript. Finally, opportunities future work and real-world evaluation have occurred and are underway.

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