A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU

Abstract

Orientation of human body segments is an important quantity in many biomechanical analyses. To get robust and drift-free 3-D orientation, raw data from miniature body worn MEMS-based inertial measurement units (IMU) should be blended in a Kalman filter. Aiming at less computational cost, this work presents a novel cascaded two-step Kalman filter orientation estimation algorithm. Tilt angles are estimated in the first step of the proposed cascaded Kalman filter. The estimated tilt angles are passed to the second step of the filter for yaw angle calculation. The orientation results are benchmarked against the ones from a highly accurate tactical grade IMU. Experimental results reveal that the proposed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions.

A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications

“…The accuracy of the proposed orientation algorithm against electromagnetic disturbances and also large external accelerations has already been investigated in our previous work [28]. Thus, the main purpose of this section is to evaluate the orientation tracking accuracy specifically during our targeted sport activities.…”

“…(17) and (28), and substituting x À 1 ðkÞ and x À 2 ðkÞ, a À (k), b À (k) and c À (k) are calculated.…”

“…Comparing to the latest orientation algorithm in the literature [22], our proposed orientation method provides similar accuracies. However, the proposed algorithm has the advantage of retaining its accuracy during temporary electromagnetic disturbances [28].…”

Proposta para confecção de implantes protéticos de braços mecânicos com custos reduzidos.

“…A calibração do giroscópio foi realizada medindo o deslocamento da leitura em cada eixo enquanto o IMU não está em movimento. O acelerômetro foi calibrado usando o procedimento descrito em [ 17] Após a calibração, a velocidade angular é medida usando o ganho do sensor e a orientação foi calculada executando uma fusão do sensor com um filtro Kalman [ 18 ]. Os dados foram adquiridos usando um Arduino Mega na frequência de 50 Hz.…”

Motion Capture Systems Using Optimal Signal Processing Algorithm: A State-of-the-art Literature

“…This research provides a comprehensive literature review for motion capture (MoCap) systems using KF algorithm. The KF can estimate the system state when it cannot be measure directly [1][2][3][4]. Tables 1-2 show the state-of-the-art literature review for MoCap systems.…”