Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

Narkhede, Parag; Poddar, Shashi; Walambe, Rahee; Ghinea, Gheorghiţă; Kotecha, Ketan

doi:10.3390/s21061937

Cited by 31 publications

(27 citation statements)

References 36 publications

(57 reference statements)

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“…The (absolute) yaw angle (hand “heading”) can not be accurately estimated using this method as the rotation axis is parallel to Earth’s gravitational field, so different yaw values would have the same influence on the accelerometer readings. Another sensor such as a magnetometer would be needed for this purpose [ 21 ]. However, the absolute yaw angle should have little value in assessing the correctness of the exercise, as any heading is possibly correct while rotational velocity around the z-axis is already included in the feature set.…”

Section: Przypomianajka V2 Designmentioning

confidence: 99%

Sensing System for Plegic or Paretic Hands Self-Training Motivation

Zubrycki

Prączko-Pawlak²,

Dominik

2022

Sensors

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Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2—a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and f1 value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.

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Section: Przypomianajka V2 Designmentioning

confidence: 99%

Sensing System for Plegic or Paretic Hands Self-Training Motivation

Zubrycki

Prączko-Pawlak²,

Dominik

2022

Sensors

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“…A natural solution in this situation seems to be the use of a complementary filtration system. The complementary filter (CF) is a computationally inexpensive and relatively efficient data fusion technique that consists of a low-pass and a high-pass filter [27]. The general structure of CF (Figure 2) consists of low-and high-frequency inputs of the composite signal.…”

Section: The Use Of a Complementary Filter For Estimation Of Headwind...mentioning

confidence: 99%

“…The non-linear CF (NCF) is based on the proportional-integral controller, in which the proportional part manages the low-frequency input and the integral part handles the high-frequency signal. 𝐾𝐾 𝑝𝑝 and 𝐾𝐾 𝑖𝑖 indicate the proportional and integral gain, respectively [27]. The non-linear complementary filter transfer function is represented by Equation ( 14).…”

Section: The Use Of a Non-linear Complementary Filter For Estimation ...mentioning

confidence: 99%

“…𝐾𝐾 𝑝𝑝 , 𝐾𝐾 𝑖𝑖 , and α are the filter constants. The proposed architecture does not require any mathematical modelling of the system and is computationally inexpensive [27]. The cascaded Taking into consideration dependencies (1), ( 2), ( 12), ( 13) and ( 17), the complete law of headwind estimation (including temporary gusts) can be formulated as (18), and finally, as Equation (19).…”

Section: The Use Of a Cascaded Complementary Filter For Estimation Of...mentioning

confidence: 99%

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Estimation of Atmospheric Gusts Using Integrated On-Board Systems of a Jet Transport Airplane—Flight Simulations

Szwed

Rzucidło²,

Rogalski³

2022

Applied Sciences

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Currently, quite accurate measurements of atmospheric gusts are carried out by airport systems only in the vicinity of the runways. There is a still open issue of availability of information about real wind gusts at cruising altitudes and during approach at a considerable distance from the airfield. Standard on-board systems of a jet transport airplane provide some information which is desirable to have knowledge of how flight parameters reflect real gust parameters and their impact on the aircraft dynamics. The paper proposes an algorithm for headwind gust magnitude estimation in relation to aircraft response. The analysed estimation algorithms assume the use of data available from the existing on-board systems only without the employment of any extra sensors or ground and satellite systems. In this way, many problems caused by different structures, configurations, and ways of installation of additional sensors and structural changes are rejected. The algorithms use the classical method for estimation of wind parameters as well as a linear longitudinal model of aircraft dynamics, taking into account the influence of wind gusts. Data fusion was realised with the use of three filtration methods. Results were evaluated to select the most accurate method of the estimation. Test data were obtained from advanced flight simulation. The experimental scenario considered a flight of a passenger twin-engine jet airplane through a layer of programmed gusts. The results of the flight simulations allowed us to determine the accuracy of the proposed gust estimation algorithms in reference to the ideal wind-speed data analysis obtained directly from the simulation environment (with the accuracy of the simulation process). The use of the proposed gust estimation algorithms may provide more accurate signal for integrated on-board systems, especially for wind shear detection and sped-up response time of flight control systems, protecting aircrafts against the adverse impact of encountered wind shear or gusts, e.g., auto-thrust or auto-throttle systems. The dedicated algorithm presented in the paper may increase the safety level of take-off and approach phases in gusty conditions and also during significant changes in wind speed at cruising altitudes in the case of crossing the area of jet stream occurrence.

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“…Several research has occurred in the area of complementary filter which aims at adapting the gain parameters of the complementary filter have been proposed in the literature in the past ( Kottath et al, 2017 ; Narkhede et al, 2019 ; H, 2019 ). A cascaded structure combining linear and non-linear version of complementary filter is presented by Narkhede et al (2021) .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Incremental learning of LSTM framework for sensor fusion in attitude estimation

Narkhede

Walambe

Poddar

et al. 2021

PeerJ Computer Science

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This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network. Gyroscope, accelerometer, and magnetometer are few widely used sensors in attitude estimation applications. Traditionally, multi-sensor fusion methods such as the Extended Kalman Filter and Complementary Filter are employed to fuse the measurements from these sensors. However, these methods exhibit limitations in accounting for the uncertainty, unpredictability, and dynamic nature of the motion in real-world situations. In this paper, the inertial sensors data are fed to the LSTM network which are then updated incrementally to incorporate the dynamic changes in motion occurring in the run time. The robustness and efficiency of the proposed framework is demonstrated on the dataset collected from a commercially available inertial measurement unit. The proposed framework offers a significant improvement in the results compared to the traditional method, even in the case of a highly dynamic environment. The LSTM framework-based attitude estimation approach can be deployed on a standard AI-supported processing module for real-time applications.

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Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

Cited by 31 publications

References 36 publications

Sensing System for Plegic or Paretic Hands Self-Training Motivation

Sensing System for Plegic or Paretic Hands Self-Training Motivation

Estimation of Atmospheric Gusts Using Integrated On-Board Systems of a Jet Transport Airplane—Flight Simulations

Incremental learning of LSTM framework for sensor fusion in attitude estimation

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