Multi-model adaptation for thigh movement estimation using accelerometers

Zhang, Zhiqiang; Ji, Lijun; Huang, Zhipei; Wu, Jiankang

doi:10.1049/iet-spr.2010.0146

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…However, this denoising is associated to special accelerometers, and accordingly, one can say the mentioned method is subjective. Hence, the Kalman filter was employed to estimate accelerometer data in the presence of noise [5]. Considering the accurate determination of statistical properties of noise measurement is difficult in actual conditions, consequently, the Kalman filter might not deliver desired performance.…”

Section: Introductionmentioning

confidence: 99%

Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm

Abbasi-Kesbi

Nikfarjam

2017

Electron. lett.

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A method for denoising accelerometer data based on the L2-norm total variation (LTV) algorithm is presented. In order to collect accelerometer data, a wireless accelerometer sensor was developed that is directly connected to a central node. By benefiting from the LTV algorithm, the obtained signals from the accelerometer are denoised. The proposed method is tested by denoising in different accelerometer signals and the results are evaluated by signal-to-noise ratio and power spectral density functions of the signals. The obtained results reveal that noise reduction has been implemented satisfactorily. Hence, the measurement accuracy of accelerometer signals for the proposed method have improved ∼4-10% than other the three low-pass filters including Savitzky-Golay, equiripple-pass-band and Butterworth.

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Section: Introductionmentioning

confidence: 99%

Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm

Abbasi-Kesbi

Nikfarjam

2017

Electron. lett.

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“…Multiple-model (MM) methods have been generally considered as the mainstream approach to manoeuvre target tracking under motion-mode uncertainty [4][5][6] and are successfully applied to a number of problems with hybrid systems (see, e.g. [7][8][9][10][11]). However, computing the optimal conditional mean state estimate requires exponential complexity for jump Markov linear systems to consider all mode histories [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Dynamic error spectrum for estimation performance evaluation: a case study on interacting multiple model algorithm

Mao

Han

Duan

2014

IET signal process.

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The commonly used root-mean-square error for estimation performance evaluation is easily dominated by large error terms. So many new alternative absolute metrics have been provided in X. R. Li's work. However, each of these metrics only reflects one narrow aspect of estimation performance, respectively. A comprehensive measure, error spectrum, was presented aggregating all these incomprehensive measures. However, when being applied to dynamic systems, this measure will have three dimensions over the total time span, which is not intuitive and difficult to be analysed. To overcome its drawbacks, a new metric, dynamic error spectrum (DES), is proposed in this study to extend the error spectrum measure to dynamic systems. Three forms under different application backgrounds are given, one of which is balanced taking into account both good and bad behaviour of an estimator and so can provide more impartial evaluation results. It can be applied to a variety of dynamic systems directly. Then the challenge in performance evaluation of the interacting multiple model (IMM) algorithm is considered, and the IMM algorithm is chosen as the testing case to illustrate the superiority of the DES metric. The simulation results validate its utility and effectiveness.

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“…However, the physical systems are often non-linear. As it is difficult to synthesise an observer for an unspecified non-linear system [11], the multiple model approach constitutes a tool which is largely used in the modelling of non-linear systems [12][13][14]. The multiple model approach consists in representing the non-linear system by an interpolation of different local linear models.…”

Section: Introductionmentioning

confidence: 99%

Multiple observer design for a non‐linear Takagi–Sugeno system submitted to unknown inputs and outputs

Bouguila

Jamel

Khedher

et al. 2013

IET signal process.

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In this paper we focus on the state estimation of a nonlinear system described by a Takagi-Sugeno multiple model submitted to unknown inputs and outputs. The proposed approach consists on a mathematical transformation which enables to consider the unknown outputs as unknown inputs that can be eliminated by a designed multiple observer. To evaluate the efficiency of the proposed approach, the convergence conditions of the state estimation error are formulated as linear matrix inequalities (LMI). Simulation Examples are given to illustrate the proposed methods.

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Multi-model adaptation for thigh movement estimation using accelerometers

Cited by 6 publications

References 20 publications

Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm

Denoising MEMS accelerometer sensors based on L2‐norm total variation algorithm

Dynamic error spectrum for estimation performance evaluation: a case study on interacting multiple model algorithm

Multiple observer design for a non‐linear Takagi–Sugeno system submitted to unknown inputs and outputs

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