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In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human–computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human–computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost. [ABSTRACT FROM AUTHOR]

To disclose the impacts of hysteretic energy (HE) demand on energy-based seismic design, this paper introduces the dimensionless parameter βEH to express the cumulative HE indirectly and establishes the βEH spectrum for energy-based seismic design. After analyzing numerous seismic responses of a single degree-of-freedom (SDOF) system, the author set up a simplified βEH spectral formula based on the genetic algorithm. Then, 750 ground motion records were selected according to Chinese site classification, and used to examine the effects of soil type and damping ratio on the βEH spectra. The results show that the soil type, site group and damping ratio have significant effects on the βEH spectra; the ductility ratio has an impact on the spectral value but not the spectral shape.

Since intravenous thrombolysis (IVT) is often associated with poor outcomes in hypertensive patients with severe acute cerebral infarction (ACI) due to occlusions of the internal carotid, basilar, or proximal middle cerebral artery, we evaluated whether multimodal intra-arterial treatment (IAT) might improve functional outcomes in this patient population. We retrospectively reviewed the charts of eligible patients who underwent multimodal IAT including intra-arterial thrombolysis, mechanical thrombectomy, balloon and/or stent angioplasty (IAT group) or IVT alone (IVT group). Outcomes included the revascularization rate 24 hours postprocedure, the frequency of survival at 7, 90, and 180 days postonset, and a measure of functional outcomes using the modified Rankin Scale (mRS). The IAT group included 62 patients and the IVT group included 31 patients. Multimodal IAT increased the revascularization rate at 24 hours (p<0.001) and the frequency of survival and functional independence (mRS ≤2) at 7 days (p<0.001 and p=0.018, respectively), 90 days (both p<0.001), and 180 days (both p<0.001). Independent predictors of longer survival were treatment with multimodal IAT (HR 0.1; 95% CI 0.0 to 0.4; p<0.001) and revascularization (HR 0.1; 95% CI 0.0 to 0.4; p<0.001), whereas a longer duration from onset to treatment was a risk factor for death (HR 1.4; 95% CI 1.2 to 1.8; p<0.001). There was no significant between-group difference for symptomatic hemorrhagic transformation. This study found that for patients with severe hypertensive ACI with large vessel occlusions, multimodal IAT improved the outcomes, including early revascularization, survival, and functional outcome. [ABSTRACT FROM AUTHOR]

Wearable human-computer interactions in daily life are increasingly encouraged by the prevalence of intelligent wearables. It poses a demanding requirement of micro-interaction and minimizing social awkwardness. Our previous work demonstrated the feasibility of recognizing silent commands through around-ear biosensors with the limitation of user adaptation. In this work, we ease the limitation by a personalization framework that integrates spectral factorization of signals, temporal confidence rejection and commonly used transfer learning algorithms. Specifically, we first empirically formulate the user adaptation issue by presenting the accuracies of applying transfer learning algorithms to our previous method. Second, we improve the signal-to-noise ratio by proposing the supervised spectral factorization method that learns the amplitude and phase mappings between around-ear signals and the signals of articulated facial muscles. Third, we leverage the time continuity of commands and introduce the time decay into confidence rejection. Finally, extensive experiments have been conducted to evaluate the feasibility and improvements. The results indicate an average accuracy of 92.38% which is significantly larger than solely using transfer learning algorithms. And a comparable accuracy can be achieved with significantly reduced data of new users. The overall performance shows the framework can significantly improve the accuracy of user adaptations. The work would aid a further step toward commercial products for silent command recognition and inspire the solution to the user adaptation challenge of wearable human-computer interactions.