Abstract By the Aharonov–Casher theorem, the Pauli operatorPhas no zero eigenvalue when the normalized magnetic flux$$\alpha $$ satisfies$$|\alpha |<1$$ , but it does have a zero energy resonance. We prove that in this case a Lieb–Thirring inequality for the$$\gamma $$ -th moment of the eigenvalues of$$P+V$$ is valid under the optimal restrictions$$\gamma \ge |\alpha |$$ and$$\gamma >0$$ . Besides the usual semiclassical integral, the right side of our inequality involves an integral where the zero energy resonance state appears explicitly. Our inequality improves earlier works that were restricted to moments of order$$\gamma \ge 1$$ . 
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                            Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds
                        
                    
    
            Abstract BackgroundImproving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy. ObjectiveThe purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging. MethodsTen young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. ResultsOn average, the normalized moment prediction root mean square error was reduced by 14.58 % ($$p=0.012$$ ) and 36.79 % ($$p<0.001$$ ) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. ConclusionsThe proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction. 
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                            - Award ID(s):
- 2002261
- PAR ID:
- 10369326
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of NeuroEngineering and Rehabilitation
- Volume:
- 19
- Issue:
- 1
- ISSN:
- 1743-0003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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