AIMC Topic: Muscle, Skeletal

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Combined Feedback Feedforward Control of a 3-Link Musculoskeletal System Based on the Iterative Training Method.

BioMed research international
The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint...

Deep learning for automatic segmentation of thigh and leg muscles.

Magma (New York, N.Y.)
OBJECTIVE: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach.

Skeletal muscle regeneration with robotic actuation-mediated clearance of neutrophils.

Science translational medicine
Mechanical stimulation (mechanotherapy) can promote skeletal muscle repair, but a lack of reproducible protocols and mechanistic understanding of the relation between mechanical cues and tissue regeneration limit progress in this field. To address th...

Individuals differ in muscle activation patterns during early adaptation to a powered ankle exoskeleton.

Applied ergonomics
Exoskeletons have the potential to assist users and augment physical ability. To achieve these goals across users, individual variation in muscle activation patterns when using an exoskeleton need to be evaluated. This study examined individual muscl...

How adaptation, training, and customization contribute to benefits from exoskeleton assistance.

Science robotics
Exoskeletons can enhance human mobility, but we still know little about why they are effective. For example, we do not know the relative importance of training, how much is required, or what type is most effective; how people adapt with the device; o...

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.

NMR in biomedicine
Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of muscles from MRI scans, which is typically done manually. In this study, we evaluated the performance o...

Machine learning approaches applied in spinal pain research.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a sum...

Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

Journal of surgical oncology
BACKGROUND: Previous studies evaluating the prognostic value of computed tomography (CT)-derived body composition data have included few patients. Thus, we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of ga...

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...

MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning.

Journal of neural engineering
This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque.The regression models, collectively known as MuscleN...