Estimation of muscle response using three-dimensional musculoskeletal models before impact situation: a simulation study.

When car crash experiments are performed using cadavers or dummies, the active muscles' reaction on crash situations cannot be observed. The aim of this study is to estimate muscles' response of the major muscle groups using three-dimensional musculoskeletal model by dynamic simulations of low-speed sled-impact. The three-dimensional musculoskeletal models of eight subjects were developed, including 241 degrees of freedom and 86 muscles. The muscle parameters considering limb lengths and the force-generating properties of the muscles were redefined by optimization to fit for each subject. Kinematic data and external forces measured by motion tracking system and dynamometer were then input as boundary conditions. Through a least-squares optimization algorithm, active muscles' responses were calculated during inverse dynamic analysis tracking the motion of each subject. Electromyography for major muscles at elbow, knee, and ankle joints was measured to validate each model. For low-speed sled-impact crash, experiment and simulation with optimized and unoptimized muscle parameters were performed at 9.4 m/h and 10 m/h and muscle activities were compared among them. The muscle activities with optimized parameters were closer to experimental measurements than the results without optimization. In addition, the extensor muscle activities at knee, ankle, and elbow joint were found considerably at impact time, unlike previous studies using cadaver or dummies. This study demonstrated the need to optimize the muscle parameters to predict impact situation correctly in computational studies using musculoskeletal models. And to improve accuracy of analysis for car crash injury using humanlike dummies, muscle reflex function, major extensor muscles' response at elbow, knee, and ankle joints, should be considered.

Intraosseous pressure and strain generated potential of cylindrical bone samples in the drained uniaxial condition for various loading rates.

Cortical bone is a composite material consisting of a porous elastic solid and viscous fluid. It is well known that the intraosseous fluid circulates as a result of a bone fluid pressure gradient in the porous space of the cortical bone. When a time-dependent mechanical load is applied to the bone, intraosseous fluid flow occurs through the interconnected pore space in the bone. Bone fluid flow leads to a strain generated streaming potential (SGP). However, there is no experimental study on the relationship between the generation of intraosseous pressure and the SGP. The purpose of this study was to obtain the relationship between SGP and intraosseous pressure generations in cortical bone. In order to understand the issue, a drained, one-dimensional experimental setup for fluid-filled cortical bone samples with four different strain rates was used to simultaneously measure the intraosseous pressure and SGP. The results revealed a significant correlation (r = 0.98, p = 0.02) between the generation of the SGP and the intraosseous pressure, which indicates that an intraosseous pressure gradient produces a SGP in cortical bone.

A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.

Relationship between socket pressure and EMG of two muscles in trans-femoral stumps during gait.

The biomechanical interaction between the leg stump and the prosthetic socket is critical in achieving close-to-normal ambulation. Although many investigations have been performed to understand the biomechanics of trans-tibial sockets, few studies have measured the socket interface pressure for transfemoral amputees. Furthermore, no report has examined how the residual muscle activities in the transfemoral stump affect the socket interface pressure characteristics during gait. In this study, an experimental method was developed to measure the trans-femoral socket interface pressures and EMG of muscles in the stumps of two trans-femoral amputees. Also, the measurement of three-dimensional prosthetic locomotion was synchronized to understand detailed socket biomechanics. Based on the experimental results, a significant correlation (P < 0.05) was found between the measured temporal EMG amplitude and the interface pressure at the knee flexor (biceps femoris) and extensor (rectus femoris). Therefore, the residual muscle activity of a trans-femoral amputee's stump could be an important factor affecting socket-interface pressure changes during ambulation.

WITHDRAWN: Somatic sensibility to mechanical vibrations on the forearms of seniors and transradial amputees.

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A supervised feature projection for real-time multifunction myoelectric hand control.

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMG pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMG signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron (MLP) classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time control system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the myoelectric hand control, are completed within 97 msec.