Load identification during object handling

Kortier, H.G.; Veltink, Petrus H.

doi:10.1109/iembs.2011.6090945

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…Identification of such a system is a typical closed loop identification problem [2,24]. However, if the following conditions are met (a mathematical derivation can be found in Appendix A), forces applied by the human body divided by the common velocity yield an approximation of the load’s impedance:

The human body is an active generator of force:

The force generated by the human body and applied to an environmental object is minimally influenced by the joint movement of both bodies and can, therefore, be considered as an independent input.

The force generated by the human body and applied to an environmental object has a sufficiently high bandwidth, that is a larger bandwidth than the load.

The load is passive with a relatively low bandwidth.

…”

Section: Methodsmentioning

confidence: 99%

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Identification of Object Dynamics Using Hand Worn Motion and Force Sensors

Kortier

Schepers

Veltink

2016

Sensors

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Emerging microelectromechanical system (MEMS)-based sensors become much more applicable for on-body measurement purposes lately. Especially, the development of a finger tip-sized tri-axial force sensor gives the opportunity to measure interaction forces between the human hand and environmental objects. We have developed a new prototype device that allows simultaneous 3D force and movement measurements at the finger and thumb tips. The combination of interaction forces and movements makes it possible to identify the dynamical characteristics of the object being handled by the hand. With this device attached to the hand, a subject manipulated mass and spring objects under varying conditions. We were able to identify and estimate the weight of two physical mass objects (0.44 kg: 29.3%±18.9% and 0.28 kg: 19.7%±10.6%) and the spring constant of a physical spring object (16.3%±12.6%). The system is a first attempt to quantify the interactions of the hand with the environment and has many potential applications in rehabilitation, ergonomics and sports.

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The human body is an active generator of force:

The force generated by the human body and applied to an environmental object is minimally influenced by the joint movement of both bodies and can, therefore, be considered as an independent input.

The force generated by the human body and applied to an environmental object has a sufficiently high bandwidth, that is a larger bandwidth than the load.

The load is passive with a relatively low bandwidth.

…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, we were able to identify and, moreover, estimate the load characteristics of a system by manipulating its position [2]. However, the instrumentation being used was rather bulky and, therefore, not suitable for on-body attachment.…”

Section: Introductionmentioning

confidence: 99%

Identification of Object Dynamics Using Hand Worn Motion and Force Sensors

Kortier

Schepers

Veltink

2016

Sensors

View full text Add to dashboard Cite

show abstract

“…Identification of such a system is a typical closed loop identification problem [53,94]. However, if the following conditions are met (a mathematical derivation can be found in Appendix 6.5.1), forces applied by the human body divided by the common velocity yield an approximation of the load's impedance:…”

Section: System Identificationmentioning

confidence: 99%

“…Subsequently, we were able to identify and, moreover, estimate the load characteristics of a system by manipulating its position [94]. However, the instrumentation being used was rather bulky and, therefore, not suitable for on-body attachment.…”

Section: Introductionmentioning

confidence: 99%

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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Load identification during object handling

Cited by 2 publications

References 2 publications

Identification of Object Dynamics Using Hand Worn Motion and Force Sensors

Identification of Object Dynamics Using Hand Worn Motion and Force Sensors

Assessment of hand kinematics and interactions with the environment

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