AI Medical Compendium Journal:
Gait & posture

Showing 11 to 20 of 49 articles

Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.

Gait & posture
BACKGROUND: Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. ...

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms.

Gait & posture
PURPOSE: Machine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropr...

A novel dataset and deep learning-based approach for marker-less motion capture during gait.

Gait & posture
BACKGROUND: The deep learning-based human pose estimation methods, which can estimate joint centers position, have achieved promising results on the publicly available human pose datasets (e.g., Human3.6 M). However, these datasets may be less effici...

The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes.

Gait & posture
BACKGROUND: Development of accessible cost-effective technology to objectively, reliably, and accurately predict musculoskeletal injury risk could aid the effort to prevent chronic pain and disability. Recent work on micro-Doppler radar suggests it m...

Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach.

Gait & posture
BACKGROUND: Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial acceler...

Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach.

Gait & posture
BACKGROUND: Inertial measurement units (IMUs) are promising tools for collecting human movement data. Model-based filtering approaches (e.g. Extended Kalman Filter) have been proposed to estimate joint angles from IMUs data but little is known about ...

Improving abnormal gait patterns by using a gait exercise assist robot (GEAR) in chronic stroke subjects: A randomized, controlled, pilot trial.

Gait & posture
BACKGROUND: Although the Gait Exercise Assist Robot (GEAR) has been reported to effectively improve gait of hemiplegic patients, no study has investigated its use in chronic stroke patients. It is possible to facilitate gait reorganization by gait tr...

A data-driven approach for detecting gait events during turning in people with Parkinson's disease and freezing of gait.

Gait & posture
BACKGROUND: Manual annotation of initial contact (IC) and end contact (EC) is a time consuming process. There are currently no robust techniques available to automate this process for Parkinson's disease (PD) patients with freezing of gait (FOG).

Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning.

Gait & posture
BACKGROUND: Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson's disease (PD). It is critical that we are able to do this accurately and as early as possible in o...

Adaptive predictive systems applied to gait analysis: A systematic review.

Gait & posture
BACKGROUND: Due to the high susceptivity of the walking pattern to be affected by several disorders, accurate analysis methods are necessary. Given the complexity and relevance of such assessment, the utilization of methods to facilitate it plays a s...