Notes

Improved catalytic hydrogen launch of quasi HKUST-1 when compared with HKUST-1.
82, and the EMG amplitude method of 0.81. Additionally, the CNN based approach showed generalizable performance, with CNN trained on one finger applicable to a different finger. The outcomes suggest that our CNN based algorithm can offer an accurate and efficient force decoding method for human-machine interactions.Previous works have shown that whitening improves the processed electromyogram (EMG) signal for use in end applications such as EMG to torque modelling. Traditional whitening methods fit each subject from calibration contractions, which is a hindrance to their widespread use. To eliminate this cumbersome calibration, a universal whitening filter was developed using the whitening filters from a pre-existing data set (64 subjects, 8 electrodes/subject). Since the shape of each subject-specific whitening filter was observed to be relatively consistent across subjects, the universal whitening filter was formed as their ensemble average. The processed EMG was then used to model surface EMG to torque about the elbow. Traditional and universal whitening provided the same EMG-torque benefit, each improving statistically over unwhitened processing by ~14% during dynamic contractions. We further studied the use of root difference of squares (RDS) post-processing to attenuate additive measurement noise in EMG channels. With and without whitening, RDS processing (vs. no RDS processing) better attenuated additive noise, reducing it from 2-4% (on average) of the processed EMG from a 50% contraction down to less then 1%. The combined use of universal whitening filters and RDS processing should be a particular benefit in real-time applications such as prosthesis control.In this paper, the validity of the stochastic model-based variance distribution of surface electromyogram (EMG) signals during isometric contraction is investigated. In the model, the EMG variance is considered as a random variable following an inverse gamma distribution, thereby allowing the representation of variations in the variance. This inverse gamma-based model for the EMG variance is experimentally validated through comparison with the empirical distribution of variances. The difference between the model distribution and the empirical distribution is quantified using the Kullback- Leibler divergence. Additionally, regression analysis is conducted between the model parameters and the statistics calculated from the empirical distribution of EMG variances. Experimental results showed that the inverse gamma-based model is potentially suitable and that its parameters can be used to evaluate the stochastic properties of the EMG variance.Identifying balance deficits associated with aging is critical to preventing falls in elderly people. The purpose of this study was to investigate the effects of aging on the multi-muscle synergy in lower extremities during standing on sloped surfaces. Eighteen healthy elderly subjects and 18 healthy young subjects were recruited in the experiment. Subjects were required to stand quietly on slopes at 5 different angles for 30 s with and without visual feedback. Surface electromyography signals of muscles in the lower limbs were collected simultaneously during standing. The muscle synergy of synergistic muscles and of antagonistic muscles were quantified by the parameters of multidimensional recurrence quantification analysis (MdRQA). The results showed that the dynamical synergies in synergistic and antagonistic muscle groups were significantly enhanced in elderly group, especially during standing on sloped surfaces (p less then 0.05). The MdRQA parameters were significantly increased when standing without visual feedback than with visual feedback (p less then 0.05). #link# The aging and the absence of visual feedback led to more deterministic structures in the dynamical coupling of muscle activations, indicating strengthened synergistic contractions of muscles. These changes in elderly may increase the restriction on balance control and reduce the adaptability of motor system to potential external perturbations. This study shed light on the effects of aging on standing balance control and may facilitate to develop novel strategies for evaluation of aging and predication of falls.Continuous observation of muscle activity could provide a comprehensive picture of the loads experienced by muscles and joints during daily life. However, a major limitation to the practical application of this approach is the need to have surface electromyography (sEMG) sensors on all involved muscles. In this work, we model the synergistic relationship between muscles as a Gaussian process enabling the inference of unmeasured muscle excitations using a subset of measured data. Specifically, we developed a model for a single subject which uses sEMG data from four leg muscles to estimate the muscle excitation time-series of six other leg muscles during level walking at a self-selected speed. The proposed technique was able to accurately estimate the held-out muscle excitation time-series of the six muscles with correlation coefficients ranging from 0.74 to 0.87 and with mean absolute error less than 3%.Vibration exercise (VE) has been suggested for effective muscle training and conditioning. Surface electromyogram (EMG) is employed as a powerful tool for the analysis of VE. However, sharp peaks are observed in the EMG spectrum. T0070907 concentration of these peaks is controversial, complicating the extraction of EMG parameters for VE analysis. The present study is therefore aiming at quantifying the relevance of these spectral peaks in EMG recording during VE. To this end, surface EMG was recorded on the biceps brachii during VE at different amplitudes and frequencies. The power percentage contained in the two narrow bands (±0.5 Hz) around the vibration frequency and its first harmonic (PPv) was calculated. The root mean square (RMS) value of the EMG was calculated with and without including the spectral peaks and then compared. The results show an average PPv value of 20.7 ± 7.9 % and a relative RMS difference (∆RMS) of 12.2 ± 3.8 %. In PPv and addition, RMS∆ seem to be influenced by vibration amplitude and frequency.
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