RSSI Signal Processing for Short-Distances Estimation: Case Study Hand Movement Detection

Due to ongoing advancements in sensor and communication technology, wearable devices have been applied to various applications, including rehabilitation, health monitoring, and body poster supervision. With the help of signal transmission, wearable nodes can communicate and transmit data over wireless channels. In fact, when wireless signals are received or transmitted, the Received Signal Strength Indicator (RSSI) is generated, which aids in signal evaluation and distance estimation between wearable nodes. Despite the advantages that RSSI can provide, it can still be inaccurate and unstable in certain situations, which makes it necessary to revise RSSI measurements before putting them into the application. In this paper, there is a proposed solution for hand movement detection using RSSI measurement. For that different filter was tested from which Kalman filter was chosen for his better result than the average filter and Savitzky Golay in short distance measurement with real-time response. Then calibrated to achieve the desired result and have better results. The Kalman filter offers a stable measurement with a low error rate when applied for hand movement detection at different positions, with an error rate of +/-2