AI Medical Compendium Journal:
Journal of biomechanics

Showing 11 to 20 of 83 articles

Exploring mechanobiology network of bone and dental tissue based on Natural Language Processing.

Journal of biomechanics
Bone and cartilage tissues are physiologically dynamic organs that are systematically regulated by mechanical inputs. At cellular level, mechanical stimulation engages an intricate network where mechano-sensors and transmitters cooperate to manipulat...

Estimating three-dimensional foot bone kinematics from skin markers using a deep learning neural network model.

Journal of biomechanics
The human foot is a complex structure comprising 26 bones, whose coordinated movements facilitate proper deformation of the foot, ensuring stable and efficient locomotion. Despite their critical role, the kinematics of foot bones during movement rema...

Identification of footstrike pattern using accelerometry and machine learning.

Journal of biomechanics
Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be d...

Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power.

Journal of biomechanics
Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and ...

The effect of time normalization and biomechanical signal processing techniques of ground reaction force curves on deep-learning model performance.

Journal of biomechanics
Time-series data are common in biomechanical studies. These data often undergo pre-processing steps such as time normalization or filtering prior to use in further analyses, including deep-learning classification. In this context, it remains unclear ...

Estimation of electrical muscle activity during gait using inertial measurement units with convolution attention neural network and small-scale dataset.

Journal of biomechanics
In general, muscle activity can be directly measured using Electromyography (EMG) or calculated with musculoskeletal models. However, both methods are not suitable for non-technical users and unstructured environments. It is desired to establish more...

Full-length radiograph based automatic musculoskeletal modeling using convolutional neural network.

Journal of biomechanics
Full-length radiographs contain information from which many anatomical parameters of the pelvis, femur, and tibia may be derived, but only a few anatomical parameters are used for musculoskeletal modeling. This study aimed to develop a fully automati...

Dynamic assessment of spine movement patterns using an RGB-D camera and deep learning.

Journal of biomechanics
In clinical practice, functional limitations in patients with low back pain are subjectively assessed, potentially leading to misdiagnosis and prolonged pain. This paper proposes an objective deep learning (DL) markerless motion capture system that u...

On the relation between gait speed and gait cycle duration for walking on even ground.

Journal of biomechanics
Gait models and reference motions are essential for the objective assessment of walking patterns and therapy progress, as well as research in the field of wearable robotics and rehabilitation devices in general. A human can achieve a desired gait spe...

Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation.

Journal of biomechanics
Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the ...