AIMC Topic: Gait Analysis

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XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes.

Scientific reports
This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63-89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and...

Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world.

Applied ergonomics
Falling injuries pose serious health risks to people of all ages, and knowing the extent of exposure to irregular surfaces will increase the ability to measure fall risk. Current gait analysis methods require overly complicated instrumentation and ha...

Effect of Robot Assisted Gait Training on Motor and Walking Function in Patients with Subacute Stroke: A Random Controlled Study.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: Robot-assisted gait training has been confirmed to have beneficial effect on the rehabilitation of stroke patients. An exoskeleton robot, named BEAR-H1, is designed to help stroke patients with walking disabilities.

Time-frequency time-space LSTM for robust classification of physiological signals.

Scientific reports
Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here ...

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometri...

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.

PloS one
INTRODUCTION: Gait deficits are debilitating in people with Parkinson's disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical featu...

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...

Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum.

Sensors (Basel, Switzerland)
Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance; however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate ...

Role of machine learning in gait analysis: a review.

Journal of medical engineering & technology
Human biomechanics and gait form an integral part of life. The gait analysis involves a large number of interdependent parameters that were difficult to interpret due to a vast amount of data and their inter-relations. To simplify evaluation, the int...

Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning.

Sensors (Basel, Switzerland)
Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessm...