Virtual reality (VR) and augmented reality (AR) are engaging interfaces that can be of benefit for rehabilitation therapy. However, they are still not widely used, and the use of surface electromyography (sEMG) signals is not established for them. Our goal is to explore whether there is a standardized protocol towards therapeutic applications since there are not many methodological reviews that focus on sEMG control/feedback. A systematic literature review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology is conducted. A Boolean search in databases was performed applying inclusion/exclusion criteria; articles older than 5 years and repeated were excluded. A total of 393 articles were selected for screening, of which 66.15% were excluded, 131 records were eligible, 69.46% use neither VR/AR interfaces nor sEMG control; 40 articles remained. Categories are, application: neurological motor rehabilitation (70%), prosthesis training (30%); processing algorithm: artificial intelligence (40%), direct control (20%); hardware: Myo Armband (22.5%), Delsys (10%), proprietary (17.5%); VR/AR interface: training scene model (25%), videogame (47.5%), first-person (20%). Finally, applications are focused on motor neurorehabilitation after stroke/amputation; however, there is no consensus regarding signal processing or classification criteria. Future work should deal with proposing guidelines to standardize these technologies for their adoption in clinical practice.
This paper comprises the methodology followed to design and implement a wavelet packet based algorithm for QRS region detection and R/S wave identification. Validation is performed using electrocardiographic (ECG) records 100 to 109 of the MIT-BIH Arrhythmia database. The proposed algorithm reconstructs the ECG signal using two nodes from the wavelet packet decomposition. Sensitivity (Se) and positive predictivity (+P) are calculated and compared to other algorithms which use approaches based on derivative signals and digital filters (Hamilton & Tompkins) as well as wavelet-based QRS detection (Li et al.), obtaining the values Se = 99.87 % and +P = 99.85 %.Only 31 false negatives were found, from a total of 21,680 heartbeats, which shows the algorithms' contribution to region location improvement. Also, once the region is determined, a simple max/min threshold is used for R/S wave identification.
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