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Vibration exercise (VE) has been suggested as an effective methodology to improve muscle strength and power performance. Several studies link the effects of vibration training to enhanced neuromuscular demand, typically ascribed to involuntary reflex mechanisms. However, the underlying mechanisms are still unclear, limiting the identification of the most appropriate vibration training protocols. This study concerns the realization of a new vibration exercise system for the upper limbs. Amplitude, frequency, and baseline of the vibrating force, which is generated by an electromechanical actuator, can be adjusted independently. A second order model is employed to identify the relation between the generated force and the input voltage driving the actuator. Our results show a high correlation (0.99) between the second order model fit and the measured data, ensuring accurate control on the supplied force. The level of neuromuscular demand imposed by the system on the targeted muscles can be estimated by electromyography (EMG). However, EMG measurements during VE can be severely affected by motion artifacts. An adaptive least mean square algorithm is proposed to remove motion artifacts from the measured EMG data. Preliminary validation with seven volunteers showed excellent motion artifact removal, enabling reliable evaluation of the neuromuscular activation.

The prominent advantage of meshfree method, is the way to build the representation of computational domain, based on the nodal points without any explicit meshing connectivity. Therefore, meshfree method can conveniently process the numerical computation inside interested domains with large deformation or inhomogeneity. In this paper, we adopt the idea of meshfree representation into cardiac medical image analysis in order to overcome the difficulties caused by large deformation and inhomogeneous materials of the heart. In our implementation, as element-free Galerkin method can efficiently build a meshfree representation using its shape function with moving least square fitting, we apply this meshfree method to handle large deformation or inhomogeneity for solving cardiac segmentation and motion tracking problems. We evaluate the performance of meshfree representation on a synthetic heart data and an in-vivo cardiac MRI image sequence. Results showed that the error of our framework against the ground truth was 0.1189 ± 0.0672 while the error of the traditional FEM was 0.1793 ± 0.1166. The proposed framework has minimal consistency constraints, handling large deformation and material discontinuities are simple and efficient, and it provides a way to avoid the complicated meshing procedures while preserving the accuracy with a relatively small number of nodes.

Abstract-Vibration exercise (VE) has been suggested to improve muscle strength and power performance, due to enhanced neuromuscular demand. However, understanding of the most appropriate VE protocols is lacking, limiting the optimal use of VE in rehabilitation programs. In this study, the fatiguing effect of vibration at different frequencies was investigated by employing a force-modulation VE system. Twenty volunteers performed 12-s isometric contractions of the biceps brachii with a load consisting of a baseline force of 80% of their maximum voluntary contraction (MVC) and a superimposed sinusoidal force at 0 (control condition with no vibration), 20, 30, and 40 Hz. Mechanical fatigue was estimated by assessment of MVC decay after each task while myoelectric fatigue was estimated by analysis of multichannel EMG signals recorded during VE. EMG conduction velocity, spectral compression, power, and fractal dimension were estimated as indicators of myoelectric fatigue. Our results suggest vibration, in particular at 30 Hz, to produce a larger degree of fatigue as compared to control condition. These results motivate further research aiming at introducing VE in rehabilitation programs with improved training protocols.