AI Medical Compendium Journal:
Journal of biomechanics

Showing 1 to 10 of 83 articles

Artificial intelligence-enhanced 3D gait analysis with a single consumer-grade camera.

Journal of biomechanics
Gait analysis is crucial for diagnosing and monitoring various healthcare conditions, but traditional marker-based motion capture (MoCap) systems require expensive equipment, extensive setup, and trained personnel, limiting their accessibility in cli...

Passive ankle and hindfoot kinematics within a robot-driven tibial movement envelope.

Journal of biomechanics
Accurate description of individual bone kinematics is essential for understanding individual foot and ankle joint function and interactions. While invasive and noninvasive techniques, including robotic simulators, have advanced the direct measurement...

AI-based human whole-body posture-prediction in continuous load reaching/leaving activities.

Journal of biomechanics
Determining worker's body posture during load handling activities is the first step toward assessing and managing occupational risk of musculoskeletal injuries. Traditional approaches for the measurement of body posture are impractical in real work s...

Neural networks can accurately identify individual runners from their foot kinematics, but fail to predict their running performance.

Journal of biomechanics
Athletes and coaches may seek to improve running performance through adjustments to running form. Running form refers to the biomechanical characteristics of a runner's movement, and can distinguish individual runners as well as groups of runners, su...

Parametric cushioning lattice insole based on finite element method and machine learning: A preliminary computational analysis.

Journal of biomechanics
The cushioning performance of insole has always been a critical consideration in its design. While the development of intelligent methods and the emergence of additive manufacturing (AM) technology have enhanced design freedom and convenience, a stan...

Validity of recurrent neural networks to predict pedal forces and lower limb kinetics in cycling.

Journal of biomechanics
Dynamic variables contribute to understand the mechanics of pedalling and can assist with injury prevention. Measuring pedal forces and joint moments and powers has a high cost, which can be mitigated by using trained artificial neural networks (ANN)...

Predictive estimation of ovine hip joint centers: Neural networks vs. linear regression.

Journal of biomechanics
The purpose of this study was to investigate the utility of neural networks to estimate the hip joint center location in sheep and compare the accuracy of neural networks to previously developed linear regression models. CT scans from 16 sheep of var...

Artificial neural networks' estimations of lower-limb kinetics in sidestepping: Comparison of full-body vs. lower-body landmark sets.

Journal of biomechanics
Artificial neural networks (ANNs) offers potential for obtaining kinetics in non-laboratory. This study compared the estimation performance for ground reaction forces (GRF) and lower-limb joint moments during sidestepping between ANNs fed with full-b...

Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: A machine learning approach.

Journal of biomechanics
Quantifying individualized rehabilitation responses and optimizing therapy for each person is challenging. For interventions like treadmill training, there are multiple parameters, such as speed or incline, that can be adjusted throughout sessions. T...

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking.

Journal of biomechanics
Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have been developed to address these issues and increase walking performance. While these devices have sho...