Abstract

We provide a novel calibration method that uses a calibration cube, a reference table, and a body sensor network (BSN) for low-cost magnetic, angular rate, and gravity (MARG) sensor systems with nine degrees of freedom (9DOF). Our recently created "Integrated Posture and Activity Network by Medit Aachen" (IPANEMA) BSN includes the 9DOF MARG sensor. The usage of the calibration cube in this novel method has the benefit of making it simple to integrate two IPANEMA BSN sensor nodes. As a result, one 9DOF MARG sensor node is utilised for calibration while the other is used for reference measurements. By processing arbitrary motions, a unique algorithm makes advantage of these measurements to enhance the calibration procedure's performance even more. Additionally, the calibration approach can be applied in an alignment process to reduce inaccuracies in the orientation of a motion capture inertial reference system and a 9DOF MARG sensor system. A two-stage experimental research is carried out to demonstrate the effectiveness of our calibrating method. The BSN data and reference tracking data are recorded for each step of the proposed calibration operation. To reduce integration errors in themovement model of the relevant body segment that is derived, the mean values of all sensor outputs are decided upon as the absolute measurement offset in the initial stage.(Keyword : Service robot, IoT, Pulse Oximeter, Patient monitoring system, Automatic sanitizer, ESP32 camera, Android App.)