Threshold-Based Low Power Consumption Human Fall Detection for Health Care and Monitoring System

The limitation of time are often seen as the primary cause of the absence of people assisting elderly. Non-invasive, inexpensive, and easy-to-use healthcare approaches becomes quite critical to support continuous health monitoring and the needs of healthcare. Smartphone is one of the devices that have limited specification. But smartphone is powerful enough to become human fall detection because it already been included with sensors and elderly has accepted and used it (especially in the future). Threshold-based low power consumption human fall detection by using accelerometer-based with four critical characteristics, Alpha Degree, and AGPeak can be the answer to solves the problem specially to achieve low power consumption computation. By using this thresholding-based fall detection method, it can be implemented in smartphone (low power device) and give fast classification result, under 0.001 second/data - below the realtime response tolerance.