A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

Rajavenkatanarayanan, Akilesh; Kanal, Varun; Tsiakas, Konstantinos; Calderon, Diane; Papakostas, Michalis; Abujelala, Maher; Galib, Marnim; Ford, James; Wylie, Glenn R.; Makedon, Fillia

doi:10.3390/mti3010006

Cited by 18 publications

(11 citation statements)

References 85 publications

(160 reference statements)

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“…Accordingly, it may be fostered with positive outcomes on his/her health conditions, with a meaningful reduction of the anxiety and/or depression levels, and with beneficial effects in terms of quality of life ( 106 , 107 ). Therefore, the caregivers, families, and health care systems may find a significant burden reduction ( 108 , 109 ). In conclusion, through this VR intervention for communication, the patients may (a) communicate their needs, (b) independently access leisure options (e.g., positive stimulation, favorite videos, amusing songs), and (c) be connected with distant relatives.…”

Section: New Communication Technologies: Virtual Reality As a Communimentioning

confidence: 99%

Virtual Reality as a Technological-Aided Solution to Support Communication in Persons With Neurodegenerative Diseases and Acquired Brain Injury During COVID-19 Pandemic

Stasolla

Matamala-Gomez

Bernini³

et al. 2021

Front. Public Health

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The COVID-19 poses an ongoing threat to lives around the world and challenges the existing public health and medical service delivery. The lockdown or quarantine measures adopted to prevent the spread of COVID-19 has caused the interruption in ongoing care and access to medical care including to patients with existing neurological conditions. Besides the passivity, isolation, and withdrawal, patients with neurodegenerative diseases experience difficulties in communication due to a limited access to leisure opportunities and interaction with friends and relatives. The communication difficulties may exacerbate the burden on the caregivers. Therefore, assistive-technologies may be a useful strategy in mitigating challenges associated with remote communication. The current paper presents an overview of the use of assistive technologies using virtual reality and virtual body ownership in providing communication opportunities to isolated patients, during COVID-19, with neurological diseases and moderate-to-severe communication difficulties. We postulate that the assistive technologies-based intervention may improve social interactions in patients with neurodegenerative diseases and acquired brain injury-thereby reducing isolation and improving their quality of life and mental well-being.

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Section: New Communication Technologies: Virtual Reality As a Communimentioning

confidence: 99%

Virtual Reality as a Technological-Aided Solution to Support Communication in Persons With Neurodegenerative Diseases and Acquired Brain Injury During COVID-19 Pandemic

Stasolla

Matamala-Gomez

Bernini³

et al. 2021

Front. Public Health

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“…Our method comes as an extension of our previous findings, originally presented in [23,24], where similar modeling solutions were deployed to predict cognitive performance on a short-term memory cognitive task. Long term scope of this exploratory research is to develop advanced computational methods for fatigue prediction and modeling able to enhance the efficiency of current approaches in assistive technologies related to medical conditions such as MS [25] or workplace training [26].…”

Section: Background-computational Modeling Of Cognitive Fatiguementioning

confidence: 99%

CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

2019

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In this work, we present CogBeacon, a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of a cognitive task inspired by the principles of the Wisconsin Card Sorting Test (WCST), a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning, and specific aspects of cognitive functioning. During each session, we record and fully annotate user EEG functionality, facial keypoints, real-time self-reports on cognitive fatigue, as well as detailed information of the performance metrics achieved during the cognitive task (success rate, response time, number of errors, etc.). Along with the dataset we provide free access to the CogBeacon data-collection software to provide a standardized mechanism to the community for collecting and annotating physiological and behavioral data for cognitive fatigue analysis. Our goal is to provide other researchers with the tools to expand or modify the functionalities of the CogBeacon data-collection framework in a hardware-independent way. As a proof of concept we show some preliminary machine learning-based experiments on cognitive fatigue detection using the EEG information and the subjective user reports as ground truth. Our experiments highlight the meaningfulness of the current dataset, and encourage our efforts towards expanding the CogBeacon platform. To our knowledge, this is the first multi-modal dataset specifically designed to assess cognitive fatigue and the only free software available to allow experiment reproducibility for multi-modal cognitive fatigue analysis.

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“…Regarding the data capture and monitoring, different technological solutions have been proposed [5]. The most popular ones are wearable devices, which have to be attached to specific places of the lower-limb of the patient, and typically capture motion data using Inertial Measurement Units (IMUs) [6]- [10] or biomedical signals such as EMG [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip

et al. 2020

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In recent years, interest in monitoring Physical Activity (PA) has increased due to its positive effect on health. New technological devices have been proposed for this purpose, mainly focused on sports, which include Machine Learning algorithms to identify the type of PA being performed. However, PA monitoring can also provide data useful for assessing the recovery process of people with impaired lower-limbs. In this work, a Machine-Learning based Physical Activity classifier design procedure is proposed, which makes use of the data provided by a Sensorized Tip that can be adapted to different Assistive Devices for Walking (ADW) such as canes or crutches. The procedure is based on three main stages: 1) defining a wide set of potential features to perform the classification; 2) optimizing the number of features by a Random-Forest approach, detecting the most relevant ones to classify five relevant activities (walking at a normal pace, walking fast, standing still, going up stairs and going down stairs); 3) training the MLbased classifiers considering the optimized feature set. A comparative analysis is carried out to evaluate the proposed procedure, using three ML-based classifier (Support Vector Machines, K-Nearest Neighbour and Artificial Neural Networks), demonstrating that the proposed approach can provide very high success rates if proper feature selection is carried out. This work presents four relevant contributions to the PA monitoring area: 1) the approach is focused on people that require ADW, which are not considered in other approaches; 2) an analysis of the features to characterize gait in people that require ADW is carried out; 3) a design procedure to optimize the number of features using a Random-Forest approach is used, avoiding a typical "brute force" procedure; and 4) a comparative analysis is carried out to demonstrate the validity of the approach.

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A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

Cited by 18 publications

References 85 publications

Virtual Reality as a Technological-Aided Solution to Support Communication in Persons With Neurodegenerative Diseases and Acquired Brain Injury During COVID-19 Pandemic

Virtual Reality as a Technological-Aided Solution to Support Communication in Persons With Neurodegenerative Diseases and Acquired Brain Injury During COVID-19 Pandemic

CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip

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