Background:
The biggest challenge in our technologically advanced society is the healthy being of aging
individuals and differently-abled people in our society. The leading cause for significant injuries and early death in senior
citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased
demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite
social and monetary impact on society. So even for the daily activity in the life of aged people, an automatically fall
detecting system and vital signs examining system become a necessity.
Objectives:
This research work aims at helping aged people and every other necessary human by monitoring their vital
signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device, could analyze the
measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate,
Heartbeat, and Temperature monitoring) for the aged and differently-abled people.
Methods:
Comparison with Present Algorithms, there are various benefits regarding privacy, success rate, and design of
devices upgraded using an implemented algorithm over the ubiquitous algorithm.
Results:
As concluded from the experimental outcomes, the accuracy achieved is up to 94%, ADXL335 is a 3-Axial
Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified
by sending a text message via GSM and GPRS module so that aged can be helped.
Conclusion:
However, a delay in the time can be noticed while comparing the gradient and minimum value to
predetermine the state of the older person. The experiment results show the adequacy of the proposed approach.
Machine Learning based Human Fall Detection System
