Continuous Description of Human 3D Motion Intent through Switching Mechanism

© 2001-2011 IEEE. Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks

Human motion intent description based on bumpless switching mechanism for rehabilitation robot.

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major difficulty of the EMG decoder modeling is to decode EMG signals with high accuracy in real-time. Our recent study presented a switching mechanism for carving up a complex task into simple subtasks and trained different submodels with low nonlinearity. However, it was observed that a "bump" behavior of decoder output (i.e., the discontinuity) occurred during the switching between two submodels. The bumps might cause unexpected impacts on the affected limb and thus potentially injure patients. To improve this undesired transient behavior on decoder outputs, we attempt to maintain the continuity of the outputs during the switching between multiple submodels. A bumpless switching mechanism is proposed by parameterizing submodels with all shared states and applied in the construction of the EMG decoder. Numerical simulation and real-time experiments demonstrated that the bumpless decoder shows high estimation accuracy in both offline and online EMG decoding. Furthermore, the outputs achieved by the proposed bumpless decoder in both testing and verification phases are significantly smoother than the ones obtained by a multimodel decoder without a bumpless switching mechanism. Therefore, the bumpless switching approach can be used to provide a smooth and accurate motion intent prediction from multi-channel EMG signals. Indeed, the method can actually prevent participants from being exposed to the risk of unpredictable loads

Measurement of finite-frequency current statistics in a single-electron transistor

Electron transport in nano-scale structures is strongly influenced by the Coulomb interaction which gives rise to correlations in the stream of charges and leaves clear fingerprints in the fluctuations of the electrical current. A complete understanding of the underlying physical processes requires measurements of the electrical fluctuations on all time and frequency scales, but experiments have so far been restricted to fixed frequency ranges as broadband detection of current fluctuations is an inherently difficult experimental procedure. Here we demonstrate that the electrical fluctuations in a single electron transistor (SET) can be accurately measured on all relevant frequencies using a nearby quantum point contact for on-chip real-time detection of the current pulses in the SET. We have directly measured the frequency-dependent current statistics and hereby fully characterized the fundamental tunneling processes in the SET. Our experiment paves the way for future investigations of interaction and coherence induced correlation effects in quantum transport.Comment: 7 pages, 3 figures, published in Nature Communications (open access

Synthesis and structure of free-standing germanium quantum dots and their application in live cell imaging

Free-standing Ge quantum dots around 3 nm in size were synthesized using a bench-top colloidal method and suspended in water and ethanol. In the ethanol solution, the photoluminescence of the Ge quantum dots was observed between 650 and 800 nm. Structural and optical properties of these colloidal Ge quantum dots were investigated by utilizing X-ray diffraction, X-ray absorption spectroscopy, Raman spectroscopy, and photoluminescence spectroscopy and transmission electron microscopy. The structure of the as-prepared Ge quantum dots that were found is best described by a core-shell model with a small crystalline core and an amorphous outer shell with a surface that was terminated by hydrogen-related species. As-prepared Ge quantum dots were suspended in cell growth medium, and then loaded into cervical carcinoma (HeLa) cells. The fluorescent microscopy images were then collected using 405 nm, 488 nm, 561 nm and 647 nm wavelengths. We observed that, based on fluorescence measurements, as-prepared Ge quantum dots can remain stable for up to 4 weeks in water. Investigation of toxicity, based on a viability test, of as-prepared uncoated Ge quantum dots in HeLa cells was carried out and compared with the commercial carboxyl coated CdSe/ZnSe quantum dots. The viability tests show that Ge quantum dots are less toxic when compared to commercial carboxyl coated CdSe/ZnS quantum dots. This journal i

Self-adaptive pre-processing methodology for big data stream mining in internet of things environmental sensor monitoring

Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). Among those multiple scenarios of DSM, the Internet of Things (IoT) plays a significant role, with a typical meaning of a tough and challenging computational case of big data. In this paper, we describe a self-adaptive approach to the pre-processing step of data stream classification. The proposed algorithm allows different divisions with both variable numbers and lengths of sub-windows under a whole sliding window on an input stream, and clustering-based particle swarm optimization (CPSO) is adopted as the main metaheuristic search method to guarantee that its stream segmentations are effective and adaptive to itself. In order to create a more abundant search space, statistical feature extraction (SFX) is applied after variable partitions of the entire sliding window. We validate and test the effort of our algorithm with other temporal methods according to several IoT environmental sensor monitoring datasets. The experiments yield encouraging outcomes, supporting the reality that picking significant appropriate variant sub-window segmentations heuristically with an incorporated clustering technique merit would allow these to perform better than others

Secreted Endothelial Cell Factors Immobilized on Collagen Scaffolds Enhance the Recipient Endothelial Cell Environment

Strategies to design novel vascular scaffolds are a continuing aim in tissue engineering and often such designs encompass the use of recombinant factors to enhance the performance of the scaffold. The established use of cell secretion utilized in feeder systems and conditioned media offer a source of paracrine factors, which has potential to be used in tissue-engineered (TE) scaffolds. Here we utilize this principle from endothelial cells (ECs), to create a novel TE scaffold by harnessing secreted factors and immobilizing these to collagen scaffolds. This research revealed increased cellular attachment and positive angiogenic gene upregulation responses in recipient ECs grown on these conditioned scaffolds. Also, the conditioning method did not affect the mechanical structural integrity of the scaffolds. These results may advocate the potential use of this system to improve vascular scaffolds' in vivo performance. In addition, this process may be a future method utilized to improve other tissue engineering scaffold therapies

Energy-aware cluster reconfiguration algorithm for the big data analytics platform spark

The development of Cloud computing and data analytics technologies has made it possible to process big data faster. Distributed computing schemes, for instance, can help to reduce the time required for data analysis and thus enhance its efficiency. However, fewer researchers have paid attention to the problem of the high-energy consumption of the cluster, placing a heavy burden on the environment, especially when the number of nodes is extremely large. As a consequence, the principle of sustainable development is violated. Considering this problem, this paper proposes an approach that can be applied to remove less-efficient nodes or to migrate over-utilized nodes of the cluster so as to adjust the load of the cluster properly and thereby achieve the goal of energy conservation. Furthermore, in order to testify the performance of the proposed methodology, we present the simulation results implemented by using CloudSim

Subcellular heterogeneity of ryanodine receptor properties in ventricular myocytes with low T-tubule density

Rationale: In ventricular myocytes of large mammals, not all ryanodine receptor (RyR) clusters are associated with T-tubules (TTs); this fraction increases with cellular remodeling after myocardial infarction (MI). Objective: To characterize RyR functional properties in relation to TT proximity, at baseline and after MI. Methods: Myocytes were isolated from left ventricle of healthy pigs (CTRL) or from the area adjacent to a myocardial infarction (MI). Ca2+ transients were measured under whole-cell voltage clamp during confocal linescan imaging (fluo-3) and segmented according to proximity of TTs (sites of early Ca2+ release, F>F50 within 20 ms) or their absence (delayed areas). Spontaneous Ca2+ release events during diastole, Ca2+ sparks, reflecting RyR activity and properties, were subsequently assigned to either category. Results: In CTRL, spark frequency was higher in proximity of TTs, but spark duration was significantly shorter. Block of Na+/Ca2+ exchanger (NCX) prolonged spark duration selectively near TTs, while block of Ca2+ influx via Ca2+ channels did not affect sparks properties. In MI, total spark mass was increased in line with higher SR Ca2+ content. Extremely long sparks (>47.6 ms) occurred more frequently. The fraction of near-TT sparks was reduced; frequency increased mainly in delayed sites. Increased duration was seen in near-TT sparks only; Ca2+ removal by NCX at the membrane was significantly lower in MI. Conclusion: TT proximity modulates RyR cluster properties resulting in intracellular heterogeneity of diastolic spark activity. Remodeling in the area adjacent to MI differentially affects these RyR subpopulations. Reduction of the number of sparks near TTs and reduced local NCX removal limit cellular Ca2+ loss and raise SR Ca2+ content, but may promote Ca2+ waves

Phase Separation and Magnetic Order in K-doped Iron Selenide Superconductor

Alkali-doped iron selenide is the latest member of high Tc superconductor family, and its peculiar characters have immediately attracted extensive attention. We prepared high-quality potassium-doped iron selenide (KxFe2-ySe2) thin films by molecular beam epitaxy and unambiguously demonstrated the existence of phase separation, which is currently under debate, in this material using scanning tunneling microscopy and spectroscopy. The stoichiometric superconducting phase KFe2Se2 contains no iron vacancies, while the insulating phase has a \surd5\times\surd5 vacancy order. The iron vacancies are shown always destructive to superconductivity in KFe2Se2. Our study on the subgap bound states induced by the iron vacancies further reveals a magnetically-related bipartite order in the superconducting phase. These findings not only solve the existing controversies in the atomic and electronic structures in KxFe2-ySe2, but also provide valuable information on understanding the superconductivity and its interplay with magnetism in iron-based superconductors