Isogeometric high order mesh generation

Exoskeletons are a promising tool to support individuals with a decreased level of motor performance. Due to their built-in sensors, exoskeletons offer the possibility of continuously recording and assessing user data, for example, related to motor performance. The aim of this article is to provide an overview of studies that rely on using exoskeletons to measure motor performance. Therefore, we conducted a systematic literature review, following the PRISMA Statement guidelines. A total of 49 studies using lower limb exoskeletons for the assessment of human motor performance were included. Of these, 19 studies were validity studies, and six were reliability studies. We found 33 different exoskeletons; seven can be considered stationary, and 26 were mobile exoskeletons. The majority of the studies measured parameters such as range of motion, muscle strength, gait parameters, spasticity, and proprioception. We conclude that exoskeletons can be used to measure a wide range of motor performance parameters through built-in sensors, and seem to be more objective and specific than manual test procedures. However, since these parameters are usually estimated from built-in sensor data, the quality and specificity of an exoskeleton to assess certain motor performance parameters must be examined before an exoskeleton can be used, for example, in a research or clinical setting.

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Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

Gait Recognition in Different Terrains with IMUs Based on Attention Mechanism Feature Fusion Method

Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction

An open source geometry kernel-based high-order element mesh generation tool

INTRODUCTION
Hemorrhagic transformation, especially symptomatic intracranial hemorrhage (sICH), is a common complication after mechanical embolectomy. This study explored a grading scale based on clinical and radiological parameters to predict sICH after mechanical embolectomy.


METHODS
Demographic and clinical data were retrospectively collected from patients with acute ischemic stroke treated with mechanical embolectomy at West China Hospital. Clinical and radiological factors associated with sICH were identified and used to develop the "STBA" grading scale. This score was then validated using data from an independent sample at the First Affiliated Hospital of Kunming Medical University.


RESULTS
We analyzed 268 patients with acute ischemic stroke who were treated with mechanical embolectomy at West China Hospital, of whom 30 (11.2%) had sICH. Patients were rated on an "STBA" score ranging from 0 to 6 based on whether systolic blood pressure was ≥145 mm Hg at admission (yes = 2 points; no = 0 points), time from acute ischemic stroke until groin puncture was ≥300 min (yes = 1; no = 0), blood glucose was ≥8.8 mmol/L (yes = 1; no = 0), and the Alberta Stroke Program Early Computed Tomography score at admission was 0-5 (2 points), 6-7 (1 point), or 8-10 (0 points). The STBA score showed good discrimination in the derivation sample (area under the receiver operating characteristic curve = 0.858) and in the validation sample (area = 0.814).


CONCLUSIONS
The STBA score may be a reliable clinical scoring system to predict sICH in acute ischemic stroke patients treated with mechanical embolectomy.

A Simple Grading Scale for Predicting Symptomatic Intracranial Hemorrhage after Mechanical Thrombectomy.


 This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

A low-cost indoor positioning system based on data-driven modeling for robotics research and education

This study examines the biomechanical effects of idealized multi-joint exoskeleton assistances on hip, knee, and ankle joints. We conducted predictive simulations of walking without assistance and with seven different assistance cases including assistance to each joint, assistance to any two joints, and assistance to all three joints. A 2D musculoskeletal model with 10 degrees of freedom and 18 muscles was used and the OpenSim Moco optimal control solver was employed for all predictive simulations, which aimed to minimize the weighted sum of several objectives including metabolic cost, muscle activation, joint coordinate acceleration, motion tracking, and whole-body center of mass (COM) acceleration. The results showed that all assistance cases changed the joint kinematics of the walking motion to different degrees and for most cases the exoskeleton assistance reduced muscle effort substantially. By comparing with the unassisted case, we found that the two cases with assistance to all three joints and to the hip-ankle joints both provided more than 50% reduction in metabolic cost of transport (COT), followed by assistance to hip-knee and knee-ankle joints with less than 40% reduction. As for the single joint assistance cases, assistance to the hip joint appeared to be the most effective with around 34% reduction in COT, followed by the assistance to the ankle joint with around 22% reduction, whereas the assistance to the knee joint was much less effective (with less than 10%).

Xianlian Alex Zhou

Papers

Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction

An open source geometry kernel-based high-order element mesh generation tool

A Simple Grading Scale for Predicting Symptomatic Intracranial Hemorrhage after Mechanical Thrombectomy.

A low-cost indoor positioning system based on data-driven modeling for robotics research and education

Prediction of walking kinematics and muscle activities under idealized lower limb exoskeleton assistances