stroke

Background

Effective retraining of foot elevation and forward propulsion is essential in stroke survivors’ gait rehabilitation. However, home-based training often lacks valuable feedback. eHealth solutions based on inertial measurement units (IMUs) could offer real-time feedback on fundamental gait characteristics. This study aimed to investigate the effect of providing real-time feedback through an eHealth solution on foot strike angle (FSA) and forward propulsion in people with stroke.

We conducted a randomized controlled clinical trial to evaluate the efficacy of a brain-computer interface ( BCI ) -based visual and motor feedback motor imagery therapy system on cognitive, psychological and limb movement in hemiplegic stroke patients. We recruited more than 100 patients and randomly divided them into three groups : conventional treatment group, MI group and MI group based on brain-computer interface. The data set contains the evaluation data of these three groups of patients before and after treatment.

This article provides an introduction to the field of datasets, including their types, characteristics, and applications. Datasets refer to collections of data that have been organized for specific purposes. They can come in various forms, including structured data, unstructured data, and semi-structured data. Each type of dataset has its own unique characteristics and uses. For example, structured data typically includes datasets that have been organized into tables and rows, such as spreadsheets or databases, while unstructured data typically includes text, images, and videos.

The proposed signals are used  for electromagnetic-based stroke classification.  Six realistic head phantom computed from MRI scans, is surrounded by an antenna array of 16 dipole antennas distributed uniformly around the head. These antennas are deployed in a fixed circular array around the head, at a distance of approximately 2-3 mm from the head.