Abstract
We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed “Integrated Posture and Activity Network by Medit Aachen” (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN. One 9DOF MARG sensor node is thereby used for calibration; the second 9DOF MARG sensor node is used for reference measurements. A novel algorithm uses these measurements to further improve the performance of the calibration procedure by processing arbitrarily-executed motions. In addition, the calibration routine can be used in an alignment procedure to minimize errors in the orientation between the 9DOF MARG sensor system and a motion capture inertial reference system. A two-stage experimental study is conducted to underline the performance of our calibration procedure. In both stages of the proposed calibration procedure, the BSN data, as well as reference tracking data are recorded. In the first stage, the mean values of all sensor outputs are determined as the absolute measurement offset to minimize integration errors in the derived movement model of the corresponding body segment. The second stage deals with the dynamic characteristics of the measurement system where the dynamic deviation of the sensor output compared to a reference system is corrected. In practical validation experiments, this procedure showed promising results with a maximum RMS error of .
Highlights
Low-cost inertial and magnetic sensors have been used in various applications, ranging from consumer electronics to medicine [1]
We presented a low-cost 9DOF MARG sensor node for our IPANEMA body sensor network (BSN)
We introduced a novel calibration algorithm, which takes into account orientation data from a body-fixed reference system
Summary
Low-cost inertial (accelerometers and gyroscopes) and magnetic sensors have been used in various applications, ranging from consumer electronics to medicine [1]. These sensors are based on micro-electro-mechanical systems (MEMS) and are either available as 3D (alternatively 2D + 1D). In biomedical or biomechanical applications, the size of the MARG sensor allows for easy attachment to the body or to a body segment. This opens new possibilities, like the independent processing of segmental orientation and short-time position that can be used in conjunction with a physically-constrained dynamic rigid body model. The result is a broad area of biomedical applications, which includes, for example, human locomotion [3,4], fall detection [5]
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