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Gait

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Gait-ViT: Gait Recognition with Vision Transformer.

Sensors (Basel, Switzerland)
Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate...

A Needs Learning Algorithm Applied to Stable Gait Generation of Quadruped Robot.

Sensors (Basel, Switzerland)
Based on Maslow's hierarchy of needs theory, we have proposed a novel machine learning algorithm that combines factors of the environment and its own needs to make decisions for different states of an agent. This means it can be applied to the gait g...

Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice.

Biomolecules
Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush injuries using standard manual gait and sensory assays, and compare them to both semi-automated as well...

Robotic Biofeedback for Post-Stroke Gait Rehabilitation: A Scoping Review.

Sensors (Basel, Switzerland)
This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating the technical and clinical specifications of biofeedback systems (BSs), including the complementary us...

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers.

Sensors (Basel, Switzerland)
Gait is a unique biometric trait with several useful properties. It can be recognized remotely and without the cooperation of the individual, with low-resolution cameras, and it is difficult to obscure. Therefore, it is suitable for crime investigati...

Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics.

Sensors (Basel, Switzerland)
Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and ...

Hybrid robot-assisted gait training for motor function in subacute stroke: a single-blind randomized controlled trial.

Journal of neuroengineering and rehabilitation
BACKGROUND: Robot-assisted gait training (RAGT) is a practical treatment that can complement conventional rehabilitation by providing high-intensity repetitive training for patients with stroke. RAGT systems are usually either of the end-effector or ...

Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets.

Journal of biomechanics
Deep learning biomechanical models perform optimally when trained with large datasets, however these can be challenging to collect in gait labs, while limited augmentation techniques are available. This study presents a data augmentation approach bas...

Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.

Sensors (Basel, Switzerland)
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the...

Is Leg-Driven Treadmill-Based Exoskeleton Robot Training Beneficial to Poststroke Patients: A Systematic Review and Meta-analysis.

American journal of physical medicine & rehabilitation
OBJECTIVE: The aim of the study is to systematically review the effects of leg-driven treadmill-based exoskeleton robot training on balance and walking ability in poststroke patients.