Application of hybrid BCI and exergames for balance rehabilitation after stroke

Muñoz, John Edison; Chavarriaga, Ricardo; Lopez, David Sebastian

doi:10.1145/2663806.2671211

Cited by 17 publications

(13 citation statements)

References 9 publications

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“…While 38% (9/24) of papers with patients did not record gender [ 23 , 44 - 47 , 57 , 59 , 60 , 70 ], the majority of patients in those that did were male and 2 had an equal distribution [ 39 , 48 ]. Meanwhile, 54% (7/13) of papers with healthy subjects did not record gender [ 52 , 53 , 55 - 57 , 59 , 60 ]; the majority of patients in those that did were male. None had more females, and only 1 had an equal distribution [ 54 ].…”

Section: Resultsmentioning

confidence: 99%

“…The types of feedback provided to patients are described in the next section. Human performance data (n=25) are those used to indicate movement or exercise performance of the individual [ 22 , 36 - 40 , 42 , 45 - 48 , 52 , 53 , 57 , 59 , 60 , 62 , 64 - 67 , 69 - 72 ]. Most of these data were generated directly from the Kinect sensor; others were from different sensors such as accelerometers.…”

Section: Resultsmentioning

confidence: 99%

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Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Dimaguila

Gray

Merolli

2018

JMIR Rehabil Assist Technol

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BackgroundPerson- or patient-generated health data (PGHD) are health, wellness, and clinical data that people generate, record, and analyze for themselves. There is potential for PGHD to improve the efficiency and effectiveness of simulated rehabilitation technologies for stroke. Simulated rehabilitation is a type of telerehabilitation that uses computer technologies and interfaces to allow the real-time simulation of rehabilitation activities or a rehabilitation environment. A leading technology for simulated rehabilitation is Microsoft’s Kinect, a video-based technology that uses infrared to track a user’s body movements.ObjectiveThis review attempts to understand to what extent Kinect-based stroke rehabilitation systems (K-SRS) have used PGHD and to what benefit.MethodsThe review is conducted in two parts. In part 1, aspects of relevance for PGHD were searched for in existing systematic reviews on K-SRS. The following databases were searched: IEEE Xplore, Association of Computing Machinery Digital Library, PubMed, Biomed Central, Cochrane Library, and Campbell Collaboration. In part 2, original research papers that presented or used K-SRS were reviewed in terms of (1) types of PGHD, (2) patient access to PGHD, (3) PGHD use, and (4) effects of PGHD use. The search was conducted in the same databases as part 1 except Cochrane and Campbell Collaboration. Reference lists on K-SRS of the reviews found in part 1 were also included in the search for part 2. There was no date restriction. The search was closed in June 2017. The quality of the papers was not assessed, as it was not deemed critical to understanding PGHD access and use in studies that used K-SRS.ResultsIn part 1, 192 papers were identified, and after assessment only 3 papers were included. Part 1 showed that previous reviews focused on technical effectiveness of K-SRS with some attention on clinical effectiveness. None of those reviews reported on home-based implementation or PGHD use. In part 2, 163 papers were identified and after assessment, 41 papers were included. Part 2 showed that there is a gap in understanding how PGHD use may affect patients using K-SRS and a lack of patient participation in the design of such systems.ConclusionsThis paper calls specifically for further studies of K-SRS—and for studies of technologies that allow patients to generate their own health data in general—to pay more attention to how patients’ own use of their data may influence their care processes and outcomes. Future studies that trial the effectiveness of K-SRS outside the clinic should also explore how patients and carers use PGHD in home rehabilitation programs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Dimaguila

Gray

Merolli

2018

JMIR Rehabil Assist Technol

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“…Three comparison tests are conducted to evaluate the performance of the software. device [33] [38] [18], hybrid device [30], evaluation investigation [13] [14], related interaction technology [9] [1] [10].…”

Section: Discussionmentioning

confidence: 99%

Intuitive Evaluation of Kinect2 based Balance Measurement Software

Lv¹,

Penades²,

Blasco³

et al. 2015

Proceedings of the 3rd 2015 Workshop on ICTs for Improving Patients Rehabilitation Research Techniques

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This is the preprint version of our paper on REHAB2015. A balance measurement software based on Kinect2 sensor is evaluated by comparing to golden standard balance measure platform intuitively. The software analysis the tracked body data from the user by Kinect2 sensor and get user's center of mass(CoM) as well as its motion route on a plane. The software is evaluated by several comparison tests, the evaluation results preliminarily prove the reliability of the software.

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“…Examples of such multimodal applications or hybrid BCIs are the “AlphaWoW” (Alpha-World of Warcraft) [ 67 ], where brainwaves in the alpha band are combined with keyboard and mouse inputs, and “Mind the Sheep!” [ 68 ], where SSVEP is combined with mouse input. Other examples include a touchless system [ 69 ], which combines eye gaze for cursor control with a BCI for making selections, and exergames with Brain Kinect Interface (BKI) for recording and analyzing motion capture signals and EEG signals in order to monitor motor recovery process [ 70 ].…”

Section: Requirements and Limitations Of Eeg-based Gaming Systemsmentioning

confidence: 99%

A Prototype SSVEP Based Real Time BCI Gaming System

Martišius

Damaševičius

2016

Computational Intelligence and Neuroscience

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Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

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Application of hybrid BCI and exergames for balance rehabilitation after stroke

Cited by 17 publications

References 9 publications

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Intuitive Evaluation of Kinect2 based Balance Measurement Software

A Prototype SSVEP Based Real Time BCI Gaming System

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