Experimental Design and Its Posterior Efficiency for the Calibration of Wearable Sensors

Author(s):Lin Ye, Steven W. Su

ABSTRACT
This paper investigates experimental design (DoE) for the calibration of the triaxial accelerometers embedded in a wearable micro Inertial Measurement Unit (μ-IMU). Firstly, a new linearization strategy is proposed for the accelerometer model associated with the so called autocalibration scheme. Then, an effective Icosahedron design is developed, which can achieve both D-optimality and G-optimality for linearized accelerometer model in ideal experimental settings. However, due to various technical limitations, it is often infeasible for the users of wearable sensors to fully implement the proposed experimental scheme. To assess the efficiency of each individual experiment, an index is given in terms of desired experimental characteristic. The proposed experimental scheme has been applied for the autocalibration of a newly developed μ-IMU.

Introduction
Wearable health monitoring system is one of the most promising technologies to provide effective solutions to health monitoring for aging populations. Various wearable sensors equipped with artificial intelligence, e.g., neural networks, fuzzy logical, genetic algorithm, particle swarm optimization, and clustering, have already been utilized for specific health monitoring tasks [1]. However, in general, the accuracy of the wearable sensors needs to be substantially enhanced in order to improve the reliability of wearable systems to meet medical device standards.

With the rapid development of Micro-Electro-Mechanical Systems (MEMS) technology, chip-based wearable sensors are becoming small, inexpensive, lightweight, and low energy-consuming, which stimulate their applications in the development of wearable systems in health monitoring [2], e.g., gait analysis and fall detection/ prediction [3]. However, due to their fabrication process, similar to most wearable sensors, MEMS sensors have large bias instability and output noise. Regular calibrations are therefore necessary to improve the accuracy of sensors’ measurements. However, due to the inaccessible of laboratory equipments, users of wearable health monitoring systems are normally unable to implement designed experiment sufficiently.

Conclusion
This study investigates the DoE for autocalibration of triaxial accelerometer in a wearable micro Inertial Measurement Unit (μ-IMU), and our contribution is two-fold. Firstly, a new model linearization strategy is proposed to linearize the nonlinear model associated with the autocalibration of triaxial accelerometer. The major technique of the proposed linearization strategy is based on recombination of parameters rather than local linearization around observation point (e.g. Taylor expansion). With such a linearized model, the classical linear model identification and DoE approaches can be applied to calibrate the triaxial accelerometer in a non-experimental environment. The second contribution is that this paper introduces a new experimental scheme, Icosahedron design. We have proved that this scheme is both G-optimal and D-optimal for the linearized 9-parameter triaxial accelerometer model. Experimental results also demonstrate that the proposed DoE scheme can significantly decrease the MSE of triaxial accelerometer after calibration. This indicates that the proposed linearization method is reliable and efficient. We believe that the proposed experimental design approach can provide an efficient tool for the users of wearable sensors to efficiently calibrate the sensors in free living condition.

Source:

Journal: Journal of Intelligent Learning Systems and Applications
DOI: 10.4236/jilsa.2015.71002 (PDF)
Paper Id: 53927 (metadata)

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