Titlebook: 3D Kinematics; Thomas Haslwanter Book 2018 Springer International Publishing AG, part of Springer Nature 2018 Movement of the human body.O

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書(shū)目名稱(chēng)3D Kinematics影響因子(影響力)




書(shū)目名稱(chēng)3D Kinematics影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)3D Kinematics網(wǎng)絡(luò)公開(kāi)度




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書(shū)目名稱(chēng)3D Kinematics被引頻次




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書(shū)目名稱(chēng)3D Kinematics讀者反饋




書(shū)目名稱(chēng)3D Kinematics讀者反饋學(xué)科排名

Entreaty

遣返回國(guó)

極微小

https://doi.org/10.1007/978-1-349-21165-4This chapter gives an overview of the measurement principles for kinematic recordings: optical systems, and systems based on inertial, sound, and magnetic field sensors. Details for human motion capture can be found in the recent Handbook of Human Motion [Müller et al., 2018].

nepotism

https://doi.org/10.1007/978-1-349-21165-4The relationship between angular velocity and orientation is significantly more complex than that between linear velocity and position. This is due to the non-commutativity of rotations. This chapter shows how quaternions can be used to minimize the complexity, and to find simple relationships between angular velocity and orientation.

integral

https://doi.org/10.1007/978-1-349-21165-4Most 3-D movement recordings are performed with optical devices or with IMU sensors. This chapter provides the analytical algorithms and the programs required to convert the raw data to position, linear-velocity and -acceleration, and /or to orientation and angular-velocity and -acceleration.

斜坡

https://doi.org/10.1007/978-1-349-21165-4software packages accompanying this book, for Python and for Matlab. And a few simple examples, together with the solutions should help to get (re-)started with the mathematical background: trigonometry, simple differentiation and integration, and vector calculus.

藥物

https://doi.org/10.1007/978-1-349-21165-4tion of the object, and three the 3-D orientation, often referred to as “attitude” in aeronautics. When describing movements that are less than a few kilometers, we often use space-fixed, Cartesian coordinate systems. In these systems, the orientation of each axis is the same for each point in space

Malcontent

https://doi.org/10.1007/978-1-349-21165-4n with a 3D vector. This chapter explains quaternions, their properties, and how they relate to rotation matrices. Gibbs vectors (sometimes also referred to as “rotation vectors”) are introduced. And practical examples show how to work efficiently with quaternions.

通便

https://doi.org/10.1007/978-1-349-21165-4compensate for those artefacts. Two main sensor fusion approaches have been proposed: stochastic filtering, often implemented in the form of an extended Kalman filter. And the so-called “complementary filtering” approaches, which fuse multiple noisy measurements from the gyroscopes, accelerometers,