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 year's, 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. Growth in medical devices can be seen in recent years. Today's medical devices aim to increase the life expectancy and comfort of human being. The systems are designed to be made reliable by improving the performance, optimizing the size and minimizing the energy consumption. For wearable technologies, power consumption is an important factor to be considered during system design. High power consumption decreases the battery life, which leads to poor comfort-ability. The purpose of this research is to develop a system with low power consumption to detect human falls before they happen.;This research points towards the development of dependable and low power embedded system device with easy to wear capabilities and optimal sensor structure. In this work, we have developed a device using motion sensor to sense the subjects linear and angular velocity, communication sensor to send the fall related information to caretaker, and signal sensors to communicate and update user about device information. The designed system is triggered on interrupts from motion sensor. As soon as the system is triggered by an interrupt signal, users balanced and unbalanced states gets monitored. Once the unbalanced state is designated, the system signifies it as fall by setting a fall flag. The fall decision parameters; pitch, roll, complementary pitch, complementary roll, Signal Vector Magnitude (SVM), and Signal Magnitude Area (SMA) are layered to classify subject's different body posture. This helps the system to differentiate between activity of daily living (ADL) and fall. 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.;The system was designed, developed, and constructed. 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 100 muA (650muA 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. The power consumption was reduced because the integrated wireless communication devices consumed power only when the fall is triggered, giving the device a potential to communicate wirelessly.
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