Falling is the most hazardous occasions that frequently occur among senior individuals, patients which requires medicinal consideration in time. Fall discovery frameworks could support senior individuals and patients to live autonomously. A programmed continuous fall discovery framework may discover fall occasions among old individuals in time that lessen the general setback rate. This device also helps to find the location of child when kidnapped which makes anyone to find out the child from kidnappers easily. The proposed framework utilizes the accelerometer and tilt sensors to structure an ongoing fall recognition framework additionally to recognize up to 4 various types of fall occasions (forward, in reverse, rightward and leftward), and compact ,wearable, ease and with high exactness rate. Machine learning algorithm is applied to predict if fall has happened or not ,so it is easy to avoid the false events effectively. Since the midriff is the focal point of gravity in the human body, the framework is progressively helpful when put at the midsection. The framework incorporates a programmed ongoing fall recognition gadget, energy utilization of sensor nodes in an IoT-based fall discovery framework and displays a structure of an altered sensor nodes and GSM texting capacity which can issue fall alert, send emergency help message
Usage of Energy Efficient Sensor Nodes on Wearable Device for fall and Child Abduction Detection
