An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking

Mazzà, Claudia; Donati, Marco; McCamley, John; Picerno, Pietro; Cappozzo, Aurelio

doi:10.1016/j.gaitpost.2011.08.024

Cited by 63 publications

(59 citation statements)

References 24 publications

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“…The aim of the study in [6] was to fine-tune a Kalman filter in order to make ideal assessments of lower trunk introduction in the frontal and sagittal planes amid treadmill strolling at distinctive paces utilizing measured straight quickening and precise speed segments. An improved no-uniformity correction algorithm based on the Kalman filter was presented in [7]. Radar information on its own is not adequate to predict the future way of vehicles in crash avoidance frameworks due to the poor estimation of their parallel traits.…”

Section: Introductionmentioning

confidence: 99%

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

et al. 2015

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Multisensor data fusion can be considered as a strong nonlinear system. A precise analytical solution is challenging to obtain, thus making it hard to dissect with routine diagnostic systems. Since tried-and-true logical systems are extremely difficult to undertake, soft computing methodologies are deemed having potential for such applications. This paper presents the support vector regression (SVR) methodology for sensor fusion to improve tracking ability. Radial basis function (RBF) and polynomial function are used as SVR kernel functions. The system combines Kalman filtering and soft computing principle, i.e., SVR, to structure an effective information combination method for the target framework. A radar-infrared system is proposed to adapt contextual changes and lessen the dubious unsettling influence of an information estimation from multisensory data. The experimental results show that an improvement in predictive accuracy and generalization capability can be achieved using the SVR with RBF kernel compared with the SVR with polynomial kernel approach.

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Section: Introductionmentioning

confidence: 99%

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

et al. 2015

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“…The ultimate goal was to design, build, and test a prototype system to integrate sensor fusion and sensor fusion algorithms to reduce engine failure rates. The aim of study [9] was the fine tuning of a Kalman filter with the intent to provide optimal estimates of lower trunk orientation in the frontal and sagittal planes during treadmill walking at different speeds using measured linear acceleration and angular velocity components represented in a local system of reference. An improved no uniformity correction algorithm based on the Kalman-filter was presented in [10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy fusion of sensor data

Petković

2014

Infrared Physics & Technology

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“…However, these systems have some shortcomings in that the set-up of these systems are not easy, measurement condition is limited and costs of these systems are very high. Therefore, many studies have been performed in measurement of lower limb joint angles and stride length and so on with inertial sensors such as a gyroscope and an accelerometer, which are small, low cost and easy for settings [1]- [9].…”

Section: Introductionmentioning

confidence: 99%

A Validation Test of Measurement Method of Lower Limb Angles Based on Kalman Filter on Different Type of Inertial Sensors

Teruyama¹,

Watanabe²

2013

IJBBB

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Abstract-Lower limb joint angle measurement method based on Kalman filter was tested using commercially available inertial sensors in our previous study for a rehabilitation use. Although the angle measurement method was effective, measurement with the wireless sensors were affected by wireless communication environment. Wired and wireless inertial sensor systems were developed in our laboratory for solving the wireless communication problem. This paper aimed to examine the 2 developed sensors, since those were different from the previously used sensors in data communication system or resolution in measurement. The 2 developed sensors and 2 commercially available sensors were evaluated in measurements of inclination and joint angles of a rigid body model comparing to the results of an optical motion measurement system. The angles were measured by correcting the angles calculated from outputs of gyroscopes based on a Kalman filter using outputs of accelerometers. All of the sensors showed small RMS errors under the static and dynamic conditions. The results suggested that various inertial sensors could measure inclination and joint angles stably if angles are calculated by using the Kalman filtering based method. It would be possible to replace sensors with inexpensive or latest commercially available sensors.Index Terms-Lower limb, angle, inertial sensor, kalman filter.

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An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking

Cited by 63 publications

References 24 publications

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

Adaptive neuro-fuzzy fusion of sensor data

A Validation Test of Measurement Method of Lower Limb Angles Based on Kalman Filter on Different Type of Inertial Sensors

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