| 1. |
|
|
| 2. |
- Munoz-Novoa, Maria, et al.
(författare)
-
Upper Limb Stroke Rehabilitation Using Surface Electromyography: A Systematic Review and Meta-Analysis
- 2022
-
Ingår i: Frontiers in Human Neuroscience. - : Frontiers Media SA. - 1662-5161. ; 16
-
Forskningsöversikt (refereegranskat)abstract
- Background: Upper limb impairment is common after stroke, and many will not regain full upper limb function. Different technologies based on surface electromyography (sEMG) have been used in stroke rehabilitation, but there is no collated evidence on the different sEMG-driven interventions and their effect on upper limb function in people with stroke. Aim: Synthesize existing evidence and perform a meta-analysis on the effect of different types of sEMG-driven interventions on upper limb function in people with stroke. Methods: PubMed, SCOPUS, and PEDro databases were systematically searched for eligible randomized clinical trials that utilize sEMG-driven interventions to improve upper limb function assessed by Fugl-Meyer Assessment (FMA-UE) in stroke. The PEDro scale was used to evaluate the methodological quality and the risk of bias of the included studies. In addition, a meta-analysis utilizing a random effect model was performed for studies comparing sEMG interventions to non-sEMG interventions and for studies comparing different sEMG interventions protocols. Results: Twenty-four studies comprising 808 participants were included in this review. The methodological quality was good to fair. The meta-analysis showed no differences in the total effect, assessed by total FMA-UE score, comparing sEMG interventions to non-sEMG interventions (14 studies, 509 participants, SMD 0.14, P 0.37, 95% CI –0.18 to 0.46, I2 55%). Similarly, no difference in the overall effect was found for the meta-analysis comparing different types of sEMG interventions (7 studies, 213 participants, SMD 0.42, P 0.23, 95% CI –0.34 to 1.18, I2 73%). Twenty out of the twenty-four studies, including participants with varying impairment levels at all stages of stroke recovery, reported statistically significant improvements in upper limb function at post-sEMG intervention compared to baseline. Conclusion: This review and meta-analysis could not discern the effect of sEMG in comparison to a non-sEMG intervention or the most effective type of sEMG intervention for improving upper limb function in stroke populations. Current evidence suggests that sEMG is a promising tool to further improve functional recovery, but randomized clinical trials with larger sample sizes are needed to verify whether the effect on upper extremity function of a specific sEMG intervention is superior compared to other non-sEMG or other type of sEMG interventions.
|
|
| 3. |
- Shaner, Sebastian W., et al.
(författare)
-
Skin stimulation and recording: Moving towards metal-free electrodes
- 2022
-
Ingår i: Biosensors and Bioelectronics: X. - : Elsevier Ltd. - 2590-1370. ; 11
-
Tidskriftsartikel (refereegranskat)abstract
- When one thinks about electrodes, especially ones meant for humans, one typically thinks of some kind of metal. Whether on the skin or in the brain, metal electrodes are characteristically expensive, stiff, non-efficient in electron-ion transduction, and prone to toxic metal ion by-products during stimulation. In order to circumvent these disadvantages, electrically-conductive laser-induced graphene (LIG) and mixed electron-ion conducting polymer (poly(3, 4‐ethylenedioxythiophene) polystyrene sulfonate – PEDOT:PSS) was leveraged to create a metal-free electrode combination that allows for an economical, soft, and organic electrode for applications on human skin. Compared to clinical-standard silver – silver chloride (Ag/AgCl) skin electrodes, the metal-free hydrogel electrodes show notable improvement in electrochemical stability and prolonged stable potentials during long-term DC stimulation (0.5–24 h). Recording and stimulation performance on human participants rivals that of Ag/AgCl, thus fortifying the notion that they are an appropriate progression to their noble metal counterparts.
|
|
| 4. |
- Wand, M., et al.
(författare)
-
Analysis of Neural Network Based Proportional Myoelectric Hand Prosthesis Control
- 2022
-
Ingår i: Ieee Transactions on Biomedical Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9294 .- 1558-2531. ; 69:7, s. 2283-2293
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: We show that state-of-the-art deep neural networks achieve superior results in regression-based multi-class proportional myoelectric hand prosthesis control than two common baseline approaches, and we analyze the neural network mapping to explain why this is the case. Methods: Feedforward neural networks and baseline systems are trained on an offline corpus of 11 able-bodied subjects and 4 prosthesis wearers, using the R-2 score as metric. Analysis is performed using diverse qualitative and quantitative approaches, followed by a rigorous evaluation. Results: Our best neural networks have at least three hidden layers with at least 128 neurons per layer; smaller architectures, as used by many prior studies, perform substantially worse. The key to good performance is to both optimally regress the target movement, and to suppress spurious movements. Due to the properties of the underlying data, this is impossible to achieve with linear methods, but can be attained with high exactness using sufficiently large neural networks. Conclusion: Neural networks perform significantly better than common linear approaches in the given task, in particular when sufficiently large architectures are used. This can be explained by salient properties of the underlying data, and by theoretical and experimental analysis of the neural network mapping. Significance: To the best of our knowledge, this work is the first one in the field which not only reports that large and deep neural networks are superior to existing architectures, but also explains this result.
|
|
