Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network
Signal processing, for delimitation of the target events and parametrization, is usually required when instrumented assessment is conducted to determine an individual’s functional status. However, these procedures may rule out relevant information obtained by sensors. To prevent this, the use of models based on neural networks that automatically extract relevant features from the raw signal may improve the characterization of the functional status. Thus, the aim of the study was to determine the classification accuracy of a multi-head convolutional layered neural network (CNN) using a simple functional mobility test in people with different conditions. The raw data from an inertial sensor e…
A new methodology based on functional principal component analysis tostudy postural stability post-stroke
[EN] Background. A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke. Methods: A rand…
Functional principal component analysis as a new methodology for the analysis of the impact of two rehabilitation protocols in functional recovery after stroke
[EN] Background: This study addressed the problem of evaluating the effectiveness of two protocols of physiotherapy for functional recovery after stroke. In particular, the study explored the use of Functional Principal Component Analysis (FPCA), a multivariate data analysis in order to assess and clarify the process of regaining independence after stroke. Methods: A randomized double-blind controlled trial was performed. Thirteen subjects with residual hemiparesis after a single stroke episode were measured in both in- and outpatient settings at a district hospital. All subjects were able to walk before suffering the stroke and were hemodynamically stable within the first week after stroke…
A new methodology for Functional Principal Component Analysis from scarce data. Application to stroke rehabilitation.
Functional Principal Component Analysis (FPCA) is an increasingly used methodology for analysis of biomedical data. This methodology aims to obtain Functional Principal Components (FPCs) from Functional Data (time dependent functions). However, in biomedical data, the most common scenario of this analysis is from discrete time values. Standard procedures for FPCA require obtaining the functional data from these discrete values before extracting the FPCs. The problem appears when there are missing values in a non-negligible sample of subjects, especially at the beginning or the end of the study, because this approach can compromise the analysis due to the need to extrapolate or dismiss subje…
Adaptive inputs in an interface for people with Dyskinetic Cerebral Palsy: Learning and usability
This study concerns the difficulty in accessing computers faced by people with Dyskinetic Cerebral Palsy (DCP). Thus diminishing their opportunities to communicate or learn. This population usually needs an alternative input human-computer interface (HCI). The paper presents an alternative multimodal HCI that incorporates a head-mounted interface and superficial electromyography sensors (sEMG). The aim of the study is to assess the usability and the suitability of these two HCI devices. Six non-disabled subjects and ten subjects with DCP participated in the iterative process in which each test follows an improvement of an input. The results indicated that for both systems, the improvements …