An optimization-based approach to human body motion capture using inertial sensors

Kok, Manon; Hol, Jeroen D.; Schön, Thomas B.

doi:10.3182/20140824-6-za-1003.02252

Cited by 106 publications

(147 citation statements)

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“…Examples include estimating the movement of a camera for visual-inertial simultaneous localisation and mapping (SLAM) [1][2][3][4] and estimation of human motion [5][6][7]. In this paper, we consider orientation estimation in three dimensions, i.e., we consider orientations that are described by the special orthogonal group SO(3).…”

Section: Introductionmentioning

confidence: 99%

On orientation estimation using iterative methods in Euclidean space

Skoglund

Sjanic

Kok

2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation. The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline. The result clearly shows that the IMEKF and the NLSbased method are superior to q-IEKF and all three outperform the non-iterative methods.

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Section: Introductionmentioning

confidence: 99%

On orientation estimation using iterative methods in Euclidean space

Skoglund

Sjanic

Kok

2017

2017 20th International Conference on Information Fusion (Fusion)

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“…The first is Kalman Filter (KF) [34], which is very popular signal processing algorithm with many applications, for example in Earth science [43], biomedical engineering [31], robotics [40], kinematic model synthesis [8,11,33,37] or object tracking [60]. The second averaging approach is based on DTW barycenter averaging (DBA) [53], which was already initially applied to movements' analysis [41,59].…”

Section: Introductionmentioning

confidence: 99%

Averaging of motion capture recordings for movements’ templates generation

Hachaj

Koptyra

Ogiela

2018

Multimed Tools Appl

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In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates' preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions Multimed Tools Appl (2018) 77:30353-30380 recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave -one -out cross -validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.

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“…This chapter follows the ideas and propositions as mentioned in Todorov [185], Kok [90] and Taetz [182]. Therefore, it pursues the descriptions that can be found in those articles.…”

Section: Introductionmentioning

confidence: 89%

“…developed a new optimization framework which is similar as the inference framework from Wu et.al. but now applied to an inertial sensor system, which has appealing properties regarding the avoidance of diverging problems and badly chosen initials [90].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of hand kinematics and interactions with the environment

Kortier¹

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The hand is one of the most important instruments of our body. Its versatility enables the execution of a wide range of tasks that ask for a powerful, precise or gentle approach. Measuring hand and finger movements, and interaction forces, is therefore important for the assessment of tasks in daily life. However, measuring on-body kinematic and kinetic quantities is a delicate procedure due to the dexterity of the hand, and moreover, the little and complex shaped skin places for sensor attachment. This thesis proposes a new on-body assessment system that allows the measurement of movements and interaction forces of the hand, fingers and thumb.The first objective, the development, evaluation and validation of an inertial and magnetic sensing system for the measurement of hand and finger kinematics is the topic of chapters 2 to 5. The second objective, assessment of the dynamic interaction between human hand and environment using combined force and movement sensing, is the topic of chapter 6.Chapter 2 describes the hardware and algorithms for a sensing system which can be attached to the hand, fingers and the thumb. The hardware consists of multiple inertial and magnetic sensors to measure angular velocities, accelerations and the magnetic field. Each individual finger and the thumb is modelled as a kinematic chain where the bones correspond to the linkages and each joint is considered as an ideal ball-socket joint. Segmental lengths were determined by manual measurement, whereas the inertial sensors provided the input for a Kalman filter to estimate the 3D orientation of the corresponding segment. Hereafter, the orientation and tip position of each finger was estimated by applying forward kinematics. To our knowledge, it is the first system that uses inertial sensors for estimating finger kinematics. The estimation quality was expressed in terms of static and dynamic accuracy, dynamic range and repeatability. Differences with an active optical reference system were found to be a maximum of 13 mm for the finger tip distance difference during circular pointing movements. A standardized test protocol for instrumented gloves showed very good repeatability results compared to other datagloves, proven by the mean angle difference of < 2 degrees. Finally, a dynamic range was specified as a measure of how well the system is able to reconstruct joint angles when experiencing large angular velocities. The system showed accurate reconstruction up to 116 full index finger flex-extension movements per minute.Chapter 3 reports an extensive comparison of our inertial sensing system against a passive opto-electronic marker system. It aims on typical hand-function tasks, including tapping, (fast) finger flexion, hand opening/closing, ab-adduction and circular pointing, which are used to quantify various motor symptoms for clinical diagnosis. Three subjects were included and instrumented with both systems. Differences in position, Range of Motion (RoM) and 3D vii joint angles were noted of which the largest were found in fast and ...

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An optimization-based approach to human body motion capture using inertial sensors

Cited by 106 publications

References 17 publications

On orientation estimation using iterative methods in Euclidean space

On orientation estimation using iterative methods in Euclidean space

Averaging of motion capture recordings for movements’ templates generation

Assessment of hand kinematics and interactions with the environment

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