AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Postural Balance

Showing 91 to 100 of 181 articles

Clear Filters

Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach.

Scientific reports
Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation im...

A Robust Balance-Control Framework for the Terrain-Blind Bipedal Walking of a Humanoid Robot on Unknown and Uneven Terrain.

Sensors (Basel, Switzerland)
Research on a terrain-blind walking control that can walk stably on unknown and uneven terrain is an important research field for humanoid robots to achieve human-level walking abilities, and it is still a field that needs much improvement. This pape...

Using supervised learning machine algorithm to identify future fallers based on gait patterns: A two-year longitudinal study.

Experimental gerontology
INTRODUCTION: Given their major health consequences in the elderly, identifying people at risk of fall is a major challenge faced by clinicians. A lot of studies have confirmed the relationships between gait parameters and falls incidence. However, a...

A controller for walking derived from how humans recover from perturbations.

Journal of the Royal Society, Interface
Humans can walk without falling despite some external perturbations, but the control mechanisms by which this stability is achieved have not been fully characterized. While numerous walking simulations and robots have been constructed, no full-state ...

Ankle torque steadiness and gait speed after a single session of robot therapy in individuals with chronic hemiparesis: a pilot study.

Topics in stroke rehabilitation
Anklebot therapy has proven to be effective in improving hemiparetic gait. However, neither ankle torque steadiness nor the relationship between changes in force control and functional tasks after therapy with Anklebot were described. To assess whet...

Dynamic neural network approach to targeted balance assessment of individuals with and without neurological disease during non-steady-state locomotion.

Journal of neuroengineering and rehabilitation
BACKGROUND: Clinical balance assessments often rely on functional tasks as a proxy for balance (e.g., Timed Up and Go). In contrast, analyses of balance in research settings incorporate quantitative biomechanical measurements (e.g., whole-body angula...

Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway.

Journal of neurology
We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute ...

Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking.

Sensors (Basel, Switzerland)
A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery...

Fast and automatic assessment of fall risk by coupling machine learning algorithms with a depth camera to monitor simple balance tasks.

Journal of neuroengineering and rehabilitation
BACKGROUND: Falls in the elderly constitute a major health issue associated to population ageing. Current clinical tests evaluating fall risk mostly consist in assessing balance abilities. The devices used for these tests can be expensive or inconven...

Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests.

The Journal of arthroplasty
BACKGROUND: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and ...