AIMC Topic: Biomechanical Phenomena

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Markerless analysis of hindlimb kinematics in spinal cord-injured mice through deep learning.

Neuroscience research
Rodent models are commonly used to understand the underlying mechanisms of spinal cord injury (SCI). Kinematic analysis, an important technique to measure dysfunction of locomotion after SCI, is generally based on the capture of physical markers plac...

A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction.

Science robotics
The application of anthropomorphic design features is widely assumed to facilitate human-robot interaction (HRI). However, a considerable number of study results point in the opposite direction. There is currently no comprehensive common ground on th...

Predictive constitutive modelling of arteries by deep learning.

Journal of the Royal Society, Interface
The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural informa...

Model-based data augmentation for user-independent fatigue estimation.

Computers in biology and medicine
OBJECTIVE: User-independent recognition of exercise-induced fatigue from wearable motion data is challenging, due to inter-participant variability. This study aims to develop algorithms that can accurately estimate fatigue during exercise.

Classifying muscle parameters with artificial neural networks and simulated lateral pinch data.

PloS one
OBJECTIVE: Hill-type muscle models are widely employed in simulations of human movement. Yet, the parameters underlying these models are difficult or impossible to measure in vivo. Prior studies demonstrate that Hill-type muscle parameters are encode...

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery.

IEEE transactions on neural networks and learning systems
Symbolic regression is a powerful technique to discover analytic equations that describe data, which can lead to explainable models and the ability to predict unseen data. In contrast, neural networks have achieved amazing levels of accuracy on image...

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions.

Sensors (Basel, Switzerland)
Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU sig...

Deep learning analysis and age prediction from shoeprints.

Forensic science international
Human gaits are the patterns of limb movements which involve both the upper and lower body parts. These patterns in terms of step rate, gait speed, stance widening, stride, and bipedal forces are influenced by different factors including environmenta...

Reducing the energy cost of running using a lightweight, low-profile elastic exosuit.

Journal of neuroengineering and rehabilitation
BACKGROUND: Human beings can enhance their distance running performance with the help of assistive devices. Although several such devices are available, they are heavy and bulky, which limits their use in everyday activities. In this study, we develo...

An image-guided hybrid robot system for dental implant surgery.

International journal of computer assisted radiology and surgery
PURPOSE: Dental implant surgery is an effective method for remediating the loss of teeth. Robot is expected to increase the accuracy of dental implant surgery. However, most of them are industrial serial robot, with low stiffness and non-unique inver...