Falls are the leading cause of fatalities or serious injuries among individuals aged 65 and older. Around 30% of elderly aged 65 and older experience falls each year. An urgent and efficient fall detection technology needs to be developed due to the rising occurrence of fall incidents in the senior population. It would offer essential assistance to the elderly in the event of a fall. An effective fall detection system is crucial for safeguarding the safety and welfare of the elderly. The human body falls due to an unstable or asymmetrical centre of gravity, which impairs its ability to maintain balance. In this study, we suggest a method for restructuring accidental falls using the symmetry principle to address the difficulty mentioned above. We use OpenPose to extract the skeletal data regarding the human body and detect falls based on three key parameters: the rate of decline at the hip joint’s centre, angle of the body’s midline with the ground, and the width-to-height proportion of the exterior rectangular shape of the human body. We are examining how people stand up after falling, a topic that prior studies have not explored.