An Advanced First Aid System Based on an Unmanned Aerial Vehicles and a Wireless Body Area Sensor Network for Elderly Persons in Outdoor Environments

Abstract

For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10−5° and 2.01 × 10−5° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient’s locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time.

Design and Implementation of Hypothermia Symptoms Early Detection With Smart Jacket Based on Wireless Body Area Network

“…Monton et al [29] proposed e-health prototype merging from five health sensors, i.e., ECG, EMG, Temperature, Airflow, and GSR. Fakhrulddin et al [30] proposed an advanced first aid system (AFAS) to monitor elderly persons from risk of falling using accelerometer sensor and heartbeat sensor. Jeon et al [31] developed wearable device called sleep care kit to measure real-time multiple biosignals such as SPO2, body temperature, and respiration to determine sleep quality and calculate the risk of sleep apnea.…”

Special Issue “Body Sensors Networks for E-Health Applications”

“…Fakhrulddin et al [ 2 ] describes another biomedical sensing system in the BSN environment. This system is applied to the detection of falls in the elderly, which have a high and increasing incidence rate.…”

“…The system is based on a BSN with sensors to measure accelerations, used in the detection of falls, and to monitor the heart rate, as a consequence of the high rate of occurrence of atrial fibrillation in the falls, associated with dizziness, fainting and the reduction of the amount of oxygen in the brain in elderly people in situations of weakness or instability. The system described in Reference [ 2 ] proposes a hybrid detection algorithm that uses both accelerations and heart rate measurement. The sensor prototype, located on the upper part of the person’s arm, includes the two bio-sensors, a microcontroller where the hybrid algorithm is run, a GPS (Global Positioning System) module for the person’s geolocation and a GSM module (Global System for Mobile Communications) to send a notification message to a call emergency center (CEC) once a fall and abnormal heart rate are detected.…”

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

“…The range-based methods determine the angles and distances between nodes in WSN. The most common techniques adopted in these localization methods include the following: time difference of arrival (TDoA) [5], angle of arrival (AoA), time of arrival (ToA) [6], acoustic energy [7], RSSI [8] and global positioning system (GPS) [9]. The TDoA and ToA methods require synchronization for all receiving nodes that are detecting the location of the target signal.…”