Wireless Sensor Networks for Fall Incident Detection: A Smart Wearable Approach Using Kalman Filter and k-NN with LoRa WAN, Node Red, and Telegram Integration
Abstract
Abstract— Falls are a common problem in many environments and affect people of all ages. Although some people fall to be minor incidents, they can have serious consequences, especially for vulnerable groups like the elderly and stroke survivors. This study aimed to develop a system for detecting falls in patients using sensor fusion and machine learning methods to accurately identify the positions of the falls. The system combines data from accelerometers and gyroscopes using the Kalman filter to categorize falls into four types: supine, prone, left, and right. The system uses the k-Nearest Neighbors (k-NN) algorithm for threshold fall motion detection to reduce false detections. A fall detection triggers the system to send the position data via LoRaWAN communication, making the data accessible through Node-RED and Telegram. The system performance was evaluated through several tests: MPU6050 sensor measurement to calibrate and respond to the Euler accelerometer and gyroscope sensor, kalman filter measurement, threshold fall detection with the k-NN algorithm measurement, and performance LoRaWAN communication. The results showed that calibrating the MPU6050 sensor effectively minimized sensor drift and noise. The implementation of the kalman filter successfully reduced noise in the sensor readings, the k-NN algorithm provided optimal system values and performance, and data transmission via LoRaWAN to Node Red and Telegram was effective.
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