The project here refers to a multi-modal fall detection system that is capable of detecting a fall from multiple vectors, each of which considers their bodies movement as well as how they fair in terms of their heart rate and SP02 Levels. The target demography for the project aims to appeal to elderly individuals or handicapped individuals that live in isolated conditions. This is assuming that the individual is incapable of taking care of themselves properly, hence the given risk of them potentially experiencing a life threatening fall. The project aims to solve this issue by introducing a multi-modal system that can actively monitor their movement and alert individuals of them experiencing a fall or being in the risk of experiencing one. The benefit of being able to actively monitor the individual is that the need for caretakers in this given context is negated hence assisting them in a financial aspect. Furthermore, the project utilizes small and portable models that are each catered to their sub-systems use cases to provide an optimal level of performance where the system can detect falls immediately and accurately. The models used and developed here refer to the following: - Heart rate and SPO2 level model - Joint Projection (Vision based) Model - Accelerometer and Gyroscope (Movement based) Model The frontend is to be hosted onto a web interface that can be moderated and hosted on any platform