AIMC Topic: Walking

Clear Filters Showing 391 to 400 of 774 articles

Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning.

Sensors (Basel, Switzerland)
Activity recognition is one of the most active areas of research in ubiquitous computing. In particular, gait activity recognition is useful to identify various risk factors in people's health that are directly related to their physical activity. One...

General Distributed Neural Control and Sensory Adaptation for Self-Organized Locomotion and Fast Adaptation to Damage of Walking Robots.

Frontiers in neural circuits
Walking animals such as invertebrates can effectively perform self-organized and robust locomotion. They can also quickly adapt their gait to deal with injury or damage. Such a complex achievement is mainly performed via coordination between the legs...

Deep neural networks enable quantitative movement analysis using single-camera videos.

Nature communications
Many neurological and musculoskeletal diseases impair movement, which limits people's function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion cap...

A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data.

Sensors (Basel, Switzerland)
The human foot is easily deformed owing to the innate form of the foot or an incorrect walking posture. Foot deformations not only pose a threat to foot health but also cause fatigue and pain when walking; therefore, accurate diagnoses of foot deform...

Mediolateral damping of an overhead body weight support system assists stability during treadmill walking.

Journal of neuroengineering and rehabilitation
BACKGROUND: Body weight support systems with three or more degrees of freedom (3-DoF) are permissive and safe environments that provide unloading and allow unrestricted movement in any direction. This enables training of walking and balance control a...

Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task.

Journal of neuroengineering and rehabilitation
BACKGROUND: Experimental designs using surrogate gait-like movements, such as in functional magnetic resonance imaging (MRI), cannot fully capture the cortical activation associated with overground gait. Overground gait in a robotic exoskeleton may b...

Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance.

Sensors (Basel, Switzerland)
Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multi...

Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study.

PloS one
Parkinson's disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems...

Cell type prioritization in single-cell data.

Nature biotechnology
We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensio...

Walking with a powered ankle-foot orthosis: the effects of actuation timing and stiffness level on healthy users.

Journal of neuroengineering and rehabilitation
BACKGROUND: In the last decades, several powered ankle-foot orthoses have been developed to assist the ankle joint of their users during walking. Recent studies have shown that the effects of the assistance provided by powered ankle-foot orthoses dep...