Inverse Kinematics for Upper Limb Compound Movement Estimation in Exoskeleton-Assisted Rehabilitation

Cortés, Camilo; Reyes-Guzmán, Ana de los; Scorza, Davide; Bertelsen, Álvaro; Carrasco, Eduardo; Gil-Agudo, Ángel; Ruiz-Salguero, Oscar; Flórez, Jesús

doi:10.1155/2016/2581924

Cited by 14 publications

(13 citation statements)

References 30 publications

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“…The NAP ω algorithm assumes the stationarity of the AoR or CoR during the segment motion. In the shoulder [ 12 , 24 , 32 ] and the elbow [ 33 ], both CoR and AoR are not completely stationary with respect to the MCS. Furthermore, during the execution of both the shoulder and elbow movements, a limited sway of the trunk could possibly occur.…”

Section: Discussionmentioning

confidence: 99%

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Functional estimation of bony segment lengths using magneto-inertial sensing: Application to the humerus

et al. 2018

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Inertial sensor technology has assumed an increasingly important role in the field of human motion analysis. However, the reliability of the kinematic estimates could still be critical for specific applications in the field of functional evaluation and motor rehabilitation. Within this context, the definition of subject-specific multi-body kinematic models is crucial since it affects the accuracy and repeatability of movement reconstruction. A key step for kinematic model calibration is the determination of bony segment lengths. This study proposes a functional approach for the in vivo estimation of the humerus length using a single magneto-inertial measurement unit (MIMU) positioned on the right distal posterior forearm. The humerus length was estimated as the distance between the shoulder elevation axis and the elbow flexion–extension axis. The calibration exercise involved five shoulder elevations in the sagittal plane with the elbow completely extended and five elbow flexion–extensions with the upper arm rigidly aligned to the trunk. Validation of the method was conducted on five healthy subjects using the humerus length computed from magnetic resonance imaging as the gold standard. The method showed mean absolute errors of 12 ± 9 mm, which were in the estimate of the humerus length. When using magneto-inertial technology, the proposed functional method represents a promising alternative to the regressive methods or manual measurements for performing kinematic model calibrations. Although the proposed methodology was validated for the estimation of the humerus length, the same approach can be potentially extended to other body segments.

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Section: Discussionmentioning

confidence: 99%

“…Depending on the specific application, segment lengths can be measured or estimated in different ways [ 5 , 7 – 12 ]. A practical approach is to establish a mathematical relationship between the body segment lengths and stature and to compute the relevant ratios ( proportionality constants reported in the anthropometric tables) [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Functional estimation of bony segment lengths using magneto-inertial sensing: Application to the humerus

et al. 2018

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“…Our method to estimate the joint angles of the human lower limb during gait is based on a previous formulation of the EIKPE method (Cortés et al, 2014 , 2016 ). In the EIKPE, the human limb and the exoskeleton are modeled as a single parallel kinematic chain connected by the fixations of the exoskeleton (Figure 5 , left).…”

Section: Methodsmentioning

confidence: 99%

“…To address this research question, we propose a modeling-experimental approach that combines personalized skeletal models of human subject with a kinematic model of the exoskeleton. We previously addressed a similar problem in the context of exoskeletons for upper limb rehabilitation (Cortés et al, 2014 , 2016 ). In that work, we formulated and assessed a computational method, denominated EIKPE (Extended Inverse Kinematics Posture Estimation), to estimate the joint angles of the human subject when the exoskeleton motion is known.…”

Section: Introductionmentioning

confidence: 99%

A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

et al. 2018

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The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.

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“…Besides that, the estimation of the human arm joint angles from the exoskeleton ones is often non-trivial if their kinematic structures differ (Nordin et al, 2014 ). Therefore, rather complex solutions have been proposed, such as extended inverse kinematics posture estimation (EIKPE) models (Wu et al, 2015 ; Cortés et al, 2016 ). Some exoskeletons even self-align the robot's active rotational axes to the user's joint axes by means of passive rotational joints, which further increases kinematic complexity (Trigili et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

2020

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End-effector-based robotic systems provide easy-to-set-up motion support in rehabilitation of stroke and spinal-cord-injured patients. However, measurement information is obtained only about the motion of the limb segments to which the systems are attached and not about the adjacent limb segments. We demonstrate in one particular experimental setup that this limitation can be overcome by augmenting an end-effector-based robot with a wearable inertial sensor. Most existing inertial motion tracking approaches rely on a homogeneous magnetic field and thus fail in indoor environments and near ferromagnetic materials and electronic devices. In contrast, we propose a magnetometer-free sensor fusion method. It uses a quaternion-based algorithm to track the heading of a limb segment in real time by combining the gyroscope and accelerometer readings with position measurements of one point along that segment. We apply this method to an upper-limb rehabilitation robotics use case in which the orientation and position of the forearm and elbow are known, and the orientation and position of the upper arm and shoulder are estimated by the proposed method using an inertial sensor worn on the upper arm. Experimental data from five healthy subjects who performed 282 proper executions of a typical rehabilitation motion and 163 executions with compensation motion are evaluated. Using a camera-based system as a ground truth, we demonstrate that the shoulder position and the elbow angle are tracked with median errors around 4 cm and 4°, respectively; and that undesirable compensatory shoulder movements, which were defined as shoulder displacements greater ±10 cm for more than 20% of a motion cycle, are detected and classified 100% correctly across all 445 performed motions. The results indicate that wearable inertial sensors and end-effector-based robots can be combined to provide means for effective rehabilitation therapy with likewise detailed and accurate motion tracking for performance assessment, real-time biofeedback and feedback control of robotic and neuroprosthetic motion support.

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Inverse Kinematics for Upper Limb Compound Movement Estimation in Exoskeleton-Assisted Rehabilitation

Cited by 14 publications

References 30 publications

Functional estimation of bony segment lengths using magneto-inertial sensing: Application to the humerus

Functional estimation of bony segment lengths using magneto-inertial sensing: Application to the humerus

A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

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