BCI and motion capture technologies for rehabilitation based on videogames

Muñoz, John Edison; Chavarriaga, Ricardo; Villada, Julian F.; SebastianLopez, David

doi:10.1109/ghtc.2014.6970312

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Additionally, VR/AR balance assessments could be used to conduct vision screenings, through the ability to measure visual acuity and identify abnormal eye movements in a dynamic environment [ 76 , 77 ]. Rehabilitation engagement could also be increased via brain–computer interfaces, which record real-time neural signals (e.g., via EEG) and transform motor intent into output commands [ 78 ]. These brain–computer interfaces allow for the development of interactive videogames/exergames, and are becoming increasingly popular in rehabilitation for populations such as those with stroke or spinal cord injury, with higher effectiveness than conventional therapies for restoring motor function [ 79 , 80 ].…”

Section: Discussionmentioning

confidence: 99%

Cognitive Task Domain Influences Cognitive-Motor Interference during Large-Magnitude Treadmill Stance Perturbations

Pitts,

Kannan,

Bhatt

2023

Sensors

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Reactive balance is postulated to be attentionally demanding, although it has been underexamined in dual-tasking (DT) conditions. Further, DT studies have mainly included only one cognitive task, leaving it unknown how different cognitive domains contribute to reactive balance. This study examined how DT affected reactive responses to large-magnitude perturbations and compared cognitive-motor interference (CMI) between cognitive tasks. A total of 20 young adults aged 18–35 (40% female; 25.6 ± 3.8 y) were exposed to treadmill support surface perturbations alone (single-task (ST)) and while completing four cognitive tasks: Target, Track, Auditory Clock Test (ACT), Letter Number Sequencing (LNS). Three perturbations were delivered over 30 s in each trial. Cognitive tasks were also performed while seated and standing (ST). Compared to ST, post-perturbation MOS was lower when performing Track, and cognitive performance was reduced on the Target task during DT (p < 0.05). There was a larger decline in overall (cognitive + motor) performance from ST for both of the visuomotor tasks compared to the ACT and LNS (p < 0.05). The highest CMI was observed for visuomotor tasks; real-life visuomotor tasks could increase fall risk during daily living, especially for individuals with difficulty attending to more than one task.

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Section: Discussionmentioning

confidence: 99%

Cognitive Task Domain Influences Cognitive-Motor Interference during Large-Magnitude Treadmill Stance Perturbations

Pitts,

Kannan,

Bhatt

2023

Sensors

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“…Na seleção dos artigos, seguindo a metodologia descrita na secção deste artigo, verificou-se que a maioria dos estudos foca tecnológicos que têm como ambição a ajuda do tratamento da mobilidade em indivíduos que sofreram AVC: 7 em 10 estudos (ABD LATIF et al, 2014;CHOI et al, 2016;HUANG et al, 2016;MUNOZ et al, 2014;LOPEZ, 2014;BAMBERG, 2012;TANG et al, 2015) um estudo é dedicado ao tratamento de escoliose, LATIF et al, 2014;CHOI et al, 2016;HUANG et al, 2016;MUNOZ et al, 2014;LOPEZ, 2014;BAMBERG, 2012;TANG et al, 2015 Nas 10 propostas contexto hospitalar (HUANG et al, 2016;MUNOZ et al, 2014) referência à existência de um sistema ou base de dados responsável por gravar os movimentos dos pacientes para a realização de uma determinada tarefa.…”

Section: Dos Dadosunclassified

“…como objetivo exercer um papel ativo no através da monitorização dos gestos, a Nas 10 propostas analisadas, 3 delas são jogos para serem jogados em (HUANG et al, 2016;MUNOZ et al, 2014) …”

Section: Dos Dadosunclassified

QUANDO JOGO ATÉ ME ESQUEÇO DAS DORES: Jogos digitais como estratégia para a reabilitação física

2018

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RESUMONão é recente a ideia de recorrer aos jogos digitais como ferramenta de apoio à reabilitação física de pacientes. As causas que afetam a mobilidade física podem ser várias desde Acidentes Vasculares Cerebrais, o envelhecimento ou traumas causados por incid jogos digitais e sistemas digitais gamificados que promovem a realização de atividade física como forma de fisioterapia de diversas patologias.

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“…Motion capture has a wide range of applications in medical rehabilitation, virtual reality, sports training and other fields [ 1 , 2 , 3 ]. However, the current inertial motion-capture devices require subjects to wear 17 or more inertial sensors [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction

Zheng

Yan

et al. 2021

Entropy

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Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25, 8.84, and 14.13, respectively.

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BCI and motion capture technologies for rehabilitation based on videogames

Cited by 12 publications

References 12 publications

Cognitive Task Domain Influences Cognitive-Motor Interference during Large-Magnitude Treadmill Stance Perturbations

Cognitive Task Domain Influences Cognitive-Motor Interference during Large-Magnitude Treadmill Stance Perturbations

QUANDO JOGO ATÉ ME ESQUEÇO DAS DORES: Jogos digitais como estratégia para a reabilitação física

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction

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