AI Medical Compendium Journal:
Medical engineering & physics

Showing 71 to 80 of 110 articles

Design and verification of a human-robot interaction system for upper limb exoskeleton rehabilitation.

Medical engineering & physics
This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human-robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman f...

A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities.

Medical engineering & physics
Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescription and injury prevention in athletes. Existing methods typically require information from multiple b...

EMG-based lumbosacral joint compression force prediction using a support vector machine.

Medical engineering & physics
Electromyography-assisted optimization (EMGAO) approach is widely used to predict lumbar joint loads under various dynamic and static conditions. However, such approach uses numerous anthropometric, kinematic, kinetic, and electromyographic data in t...

Validation of computerized square-drawing based evaluation of motor function in patients with stroke.

Medical engineering & physics
Human-administered clinical scales are commonly used for quantifying motor performance and determining the course of therapy in post-stroke individuals. Computerized methods aim to improve consistency, resolution and duration of patients' evaluation....

Automated robot-assisted assessment for wrist active ranges of motion.

Medical engineering & physics
The measurement of wrist active range of motion (ROM) is essential for determining the progress of hand functional recovery, which can provide insight into quantitative improvements and enable effective monitoring during hand rehabilitation. Compared...

Machine learning algorithms for predicting scapular kinematics.

Medical engineering & physics
The goal of this study was to develop and validate a non-invasive approach to estimate scapular kinematics in individual patients. We hypothesized that machine learning algorithms could be developed using motion capture data to accurately estimate dy...

Artificial neural networks in the selection of shoe lasts for people with mild diabetes.

Medical engineering & physics
This research addressed the selection of shoe lasts for footwear design to help relieve the pain associated with diabetic neuropathy and foot ulcers. A reverse engineering (RE) technique was used to convert point clouds corresponding to scanned shoe ...

Dual-mode artificially-intelligent diagnosis of breast tumours in shear-wave elastography and B-mode ultrasound using deep polynomial networks.

Medical engineering & physics
The main goal of this study is to build an artificial intelligence (AI) architecture for automated extraction of dual-modal image features from both shear-wave elastography (SWE) and B-mode ultrasound, and to evaluate the AI architecture for classifi...

Robotic hip joint testing: Development and experimental protocols.

Medical engineering & physics
The use of robotic systems combined with force sensing is emerging as the gold standard for in vitro biomechanical joint testing, due to the advantage of controlling all six degrees of freedom independently of one another. This paper describes a nove...

Development and validation of a robotic system for ankle joint testing.

Medical engineering & physics
Ankle sprains are the most common sports injury. Gaining a better understanding of ankle mechanics will help improve current treatments, enabling a better quality of life for patients following surgery. In this paper, the development of a robotic sys...