Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation

Marin-Pardo, Octavio; Vourvopoulos, Athanasios; Neureither, Meghan; Saldaña, David; Jahng, Esther; Liew, Sook‐Lei

doi:10.1007/978-3-030-23522-2_35

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Vourvopoulos et al [64] presented a head-mounted braincomputer interface (BCI) for post-stroke rehabilitation based on the REINVENT system [65]. The authors of [66] compared the performance of EMG and EEG signals used as biofeedback in VR-based rehabilitation and found participants performing much better with EMG than with EEG feedback. A summary of the robot-assisted and VR-based works are presented in Table II and Table III, respectively.…”

Section: B Virtual Reality Aided Post Stroke Rehabilitation Systemsmentioning

confidence: 99%

“…For instance, one critical feature is automatically detecting when the patient or user of the system is experiencing discomfort [123]. Ergonomics of the devices used in the system is another area of concern, with many of the works, such as [45], [46], [59], [66] reporting on the users' affinity with the system and ease of use. The system's stability is also a significant concern, given that the peripherals involved in the system are in very close proximity with the users.…”

Section: Challenges In Automatic Stroke Rehabilitationmentioning

confidence: 99%

See 1 more Smart Citation

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapistlike assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

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Section: B Virtual Reality Aided Post Stroke Rehabilitation Systemsmentioning

confidence: 99%

Section: Challenges In Automatic Stroke Rehabilitationmentioning

confidence: 99%

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…The acquired post-stroke EEG signals that indicate an attempt to move, drive a virtual avatar arm (from the affected side), allowing patient-driven action observation BCI in VR. They show that EEG-based BCI-VR may benefit patients with more severe motor impairments, by increasing their communication bandwidth to VR, in-contrast to patients with more mild impairments that they can still harness existing sensorimotor pathways with EMG-based feedback in the same VR training (Marin-Pardo et al, 2019).…”

Section: Research Highlightsmentioning

confidence: 99%