Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Passon A

PROVIDER: S-EPMC7806092 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Passon Arne A Schauer Thomas T Seel Thomas T

Frontiers in robotics and AI 20201127

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 trackin ...[more]

PMID: 33501318

Similar Datasets

Project description:BACKGROUND:End-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient's hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic configuration of the upper limb during the therapy. However, the Range of Motion (ROM) together with the clinical assessment scales offers a comprehensive assessment to the therapist. Our aim is to present a robust and stable kinematic reconstruction algorithm to accurately measure the upper limb joints using only an accelerometer placed onto the upper arm. METHODS:The proposed algorithm is based on the inverse of the augmented Jaciobian as the algorithm (Papaleo, et al., Med Biol Eng Comput 53(9):815-28, 2015). However, the estimation of the elbow joint location is performed through the computation of the rotation measured by the accelerometer during the arm movement, making the algorithm more robust against shoulder movements. Furthermore, we present a method to compute the initial configuration of the upper limb necessary to start the integration method, a protocol to manually measure the upper arm and forearm lengths, and a shoulder position estimation. An optoelectronic system was used to test the accuracy of the proposed algorithm whilst healthy subjects were performing upper limb movements holding the end effector of the seven Degrees of Freedom (DoF) robot. In addition, the previous and the proposed algorithms were studied during a neuro-rehabilitation therapy assisted by the 'PUPArm' planar robot with three post-stroke patients. RESULTS:The proposed algorithm reports a Root Mean Square Error (RMSE) of 2.13cm in the elbow joint location and 1.89cm in the wrist joint location with high correlation. These errors lead to a RMSE about 3.5 degrees (mean of the seven joints) with high correlation in all the joints with respect to the real upper limb acquired through the optoelectronic system. Then, the estimation of the upper limb joints through both algorithms reveal an instability on the previous when shoulder movement appear due to the inevitable trunk compensation in post-stroke patients. CONCLUSIONS:The proposed algorithm is able to accurately estimate the human upper limb joints during a neuro-rehabilitation therapy assisted by end-effector robots. In addition, the implemented protocol can be followed in a clinical environment without optoelectronic systems using only one accelerometer attached in the upper arm. Thus, the ROM can be perfectly determined and could become an objective assessment parameter for a comprehensive assessment.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets