Recent advances in artificial intelligence methods have allowed improved disease diagnosis using fast and low-cost protocols. The present study explored the potential of different deep neural networks (DNNs) and transfer learning methods to detect kn...
Clinical gait analysis (CGA) has historically relied on clinician experience and judgment, leading to modest, stagnant, and unpredictable outcomes. This paper introduces Evidence-Based Gait Analysis Interpretation Tools (EB-GAIT), a novel framework l...
INTRODUCTION: Robotic Exoskeleton-assisted gait training is an emerging approach in spinal cord injury (SCI) rehabilitation. This case report evaluates the effectiveness of Powered-Robotic exoskeleton-based gait training in an individual with chronic...
Digital technologies for monitoring motor symptoms of Parkinson's Disease (PD) underwent a strong evolution during the past years. Although it has been shown for several devices that derived digital gait features can reliably discriminate between hea...
Deep learning has become powerful and yet versatile tool that allows for the extraction of complex patterns from rich datasets. One field that can benefits from this advancement is human gait analysis. Conventional gait analysis requires a specialize...
In this study, an Electrical grid-independent Machine learning-assisted Wearable device for Gait analysis (EMWG) with a ground reaction force sensor is presented. For gait analysis, a multi-layer perceptron is identified as the optimal model among va...
Deep-neural-network-based artificial intelligence enables quantitative gait analysis with commodity sensors. However, current gait-analysis models are usually specialized for specific clinical populations and sensor settings due to the limited size a...
OBJECTIVE: Depression in older adults is a growing public health concern, yet there is still a lack of convenient and real-time methods for depressive symptoms identification. This study aims to develop a gait-based depression recognition method for ...
Anxiety and depression disorders are increasingly common, necessitating methods for real-time assessment and early identification. This study investigates gait analysis as a potential indicator of mental health, using the Microsoft Kinect sensor to c...
Accurate recognition of human activities from gait sensory data plays a vital role in healthcare and wellness monitoring. However, conventional deep learning models for Human Activity Recognition (HAR) often require large labeled datasets and extensi...
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