Portable and Low Power Efficient Pre-Fall Detection Methodology

Abstract

Fall in recent years have become a potential threat to elder generation. It occurs because of side effects of medication, lack of physical activities, limited vision, and poor mobility. Looking at the problems faced by people and cost of treatment after falling, it is of high importance to develop a system that will help in detecting the fall before it occurs. Over the years, this has influenced researchers to pursue the development to automatic fall detection system. However, much of existing work achieved a hardware system to detect pre and post fall patterns, the existing systems deficient in achieving low power consumption, user-friendly hardware implementation and high precision on a single portable system. This research points towards the development of dependable and low power embedded system device with easy to wear capabilities and optimal sensor structure. The designed system is triggered on interrupts from motion sensor to monitor users balanced, and unbalanced states. The fall decision parameters; pitch, roll, Signal Vector Magnitude (SVM), and Signal Magnitude Area (SMA) are layered to classify subject's different body posture. When the fall flag is set, the device sends important information like GPS location and fall type to caretaker. Early fall detection gives milliseconds of time to initiates the preventive measures. Near 100% sensitivity, 96% accuracy, and 95% specificity for fall detection were measured. The system can detect Front, Back, Side and Stair fall with consumption of 100uA (650uA with BLE consumption) in deep sleep mode, 6.5mA in active mode with no fall, and 14.5mA, of which 8.5 mA is consumed via the BLE when fall is declared in active mode.

Possible Life Saver: A Review on Human Fall Detection Technology

“…FMFP: Sensitivity = 97.8% Specificity = 99.1% SMFD: Sensitivity = 98.6% Specificity = 99.3% Works on only one sensor which fails to provide accuracy for different types of falling events. [39] A belt-like wearable sensor based on pre-fall detection system, which uses linear and angular velocity information from motion sensor to classify human fall. Accuracy = 96.63% Sensitivity = 100% Specificity = 95.45%…”