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Gait clinical trials

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NCT ID: NCT06289231 Not yet recruiting - Gait Clinical Trials

The Use of Artificial Intelligence (AI) for Gait Analysis

Start date: May 2024
Phase:
Study type: Observational

The main purpose of this study will be to assess the consistency and reliability of measurements made using the Vicon three-plane gait analysis device (Vicon Motion Capture System Ltd, Oxford, UK) and a mobile application based on image recognition technology with the help of artificial intelligence.

NCT ID: NCT05786690 Not yet recruiting - Gait Clinical Trials

The Effect of Microprocessor Controlled Prostheses on Walking Pattern and Energy Consumption

Start date: March 23, 2023
Phase:
Study type: Observational

The aim of this study was to investigate MIC and non-MIC prostheses in terms of gait pattern and energy consumption in above-the-knee amputee patients.

NCT ID: NCT05713383 Not yet recruiting - Gait Clinical Trials

The Role of Perturbed Auditory Information for Self-motion in Gait

Start date: July 1, 2024
Phase: N/A
Study type: Interventional

As people walk and interact with objects such as when opening a door, their movements make sounds. It is possible that these sounds are also used as feedback to stabilize and adapt movement. There is some evidence for such a connection between the auditory and motor systems in activities of daily living, yet the empirical work is insufficient because the role of the auditory system in movement is a relatively neglected topic. The objective of this study is to address this gap. The study will also evaluate the potential for improvements in movement stability and variability by restricting or augmenting the auditory feedback from the participants' footstep sounds.

NCT ID: NCT05443893 Not yet recruiting - Gait Clinical Trials

Artificial Intelligence in Kinematics Analysis

Start date: July 10, 2022
Phase:
Study type: Observational

1. Establish data sets. The private data set includes relevant parameters including video of the subject's gait and standard methods for kinematic analysis; 2. Develop new models. Based on public and private data sets, the kinematic analysis model of human key point detection is further developed. 3. Test the new model. By comparing the parameters with the standard method, the accuracy of the model was verified, and the kinematics analysis model of artificial intelligence with accuracy above 98% was obtained

NCT ID: NCT04944342 Not yet recruiting - Covid19 Clinical Trials

Breathing After COVID-19

Start date: June 25, 2021
Phase:
Study type: Observational

This research; It will be done to perform thoracic movement analysis of young adult individuals who have survived COVID-19 and to compare them with those without a history of COVID-19.

NCT ID: NCT01223781 Not yet recruiting - Parkinson Disease Clinical Trials

Biofeedback-based Motor Learning to Ameliorate Freezing of Gait

Start date: January 2011
Phase: N/A
Study type: Interventional

Objective/Rationale: The investigators objective is to demonstrate that an intervention program based on motor learning principles can be applied to train subjects with Parkinson's disease (PD) who suffer from freezing to walk in a way that minimizes the occurrence of freezing. Since sufficient motor learning capabilities are preserved in PD, the investigators hypothesize that an intervention program that targets the time periods just prior to an approaching freezing episode can modify the walking strategies so that the episode will now be averted. Project Description: The freezing burden will be quantified in subjects with PD before and after 6 weeks of training. Two types of interventions (20 subjects in each group) will be tested: 1) Open-loop group (OLG); 2) Closed-loop group (CLG). Each session of the OLG training includes walking courses aimed at provoking freezing episodes. The experimenter will trigger an auditory rhythmic stimulation (RAS) in walking conditions likely to invoke freezing (e.g., turning) and the subject will learn to synchronize his/her gait with the auditory cues, i.e., to keep the walking pace and coordination and, as a result, to avoid freezing. Similar principles will apply for the CLG training; however, the RAS will be elicited automatically by a device that recognizes an approaching freezing episode. Relevance to Diagnosis/Treatment of Parkinson's Disease: If even partially successful, the investigators will show, for the first time that freezing of gait is amenable to motor learning and that appropriate training with external cueing can alleviate these motor blockades. While future studies will be needed to further assess long-term efficacy and other important questions about clinical efficacy and the mechanisms involved, this study should go a long way towards improving the investigators understanding of freezing of gait and its amenability to appropriate therapy. Anticipated Outcome: The investigators anticipate that after intensive training, the central nervous system (CNS) of subjects with PD will be able to anticipate impending freezing episodes based on awareness of the environmental conditions (e.g., an approaching turn) and/or based on sub-conscious response to a deteriorating gait pattern. As a result, an automated motor response that paces and coordinates gait will be internally triggered by the CNS and the approaching freezing episode will be averted. The overall freezing burden will therefore decrease in trained subjects.