The medical image registration between preoperative three-dimensional (3D) scan data and intraoperative two-dimensional (2D) image is a key technology in the surgical navigation. Most previous methods need to generate 2D digitally reconstructed radiographs (DRR) images from the 3D scan volume data, then use conventional image similarity function for comparison. This procedure includes a large amount of calculation and is difficult to archive real-time processing. In this paper, with using geometric feature and image density mixed characteristics, we proposed a new similarity measure function for fast 2D-3D registration of preoperative CT and intraoperative X-ray images. This algorithm is easy to implement, and the calculation process is very short, while the resulting registration accuracy can meet the clinical use. In addition, the entire calculation process is very suitable for highly parallel numerical calculation by using the algorithm based on CUDA hardware acceleration to satisfy the requirement of real-time application in surgery.
In order to evaluate the ability of human standing balance scientifically, we in this study proposed a new evaluation method based on the chaos nonlinear analysis theory. In this method, a sinusoidal acceleration stimulus in forward/backward direction was forced under the subjects' feet, which was supplied by a motion platform. In addition, three acceleration sensors, which were fixed to the shoulder, hip and knee of each subject, were applied to capture the balance adjustment dynamic data. Through reconstructing the system phase space, we calculated the largest Lyapunov exponent (LLE) of the dynamic data of subjects' different segments, then used the sum of the squares of the difference between each LLE (SSDLLE) as the balance capabilities evaluation index. Finally, 20 subjects' indexes were calculated, and compared with evaluation results of existing methods. The results showed that the SSDLLE were more in line with the subjects' performance during the experiment, and it could measure the body's balance ability to some extent. Moreover, the results also illustrated that balance level was determined by the coordinate ability of various joints, and there might be more balance control strategy in the process of maintaining balance.
An unequal loss of peripheral vision may happen with high sustaining multi-axis acceleration, leading to a great potential flight safety hazard. In the present research, finite element method was used to study the mechanism of unequal loss of peripheral vision. Firstly, a 3D geometric model of skull was developed based on the adult computer tomography (CT) images. The model of double eyes was created by mirroring with the previous right eye model. Then, the double-eye model was matched to the skull model, and fat was filled between eyeballs and skull. Acceleration loads of head-to-foot (Gz), right-to-left (Gy), chest-to-back (Gx) and multi-axis directions were applied to the current model to simulate dynamic response of retina by explicit dynamics solution. The results showed that the relative strain of double eyes was 25.7% under multi-axis acceleration load. Moreover, the strain distributions showed a significant difference among acceleration loaded in different directions. It indicated that a finite element model of double eyes was an effective means to study the mechanism of an unequal loss of peripheral vision at sustaining high multi-axis acceleration.
Dual-energy computed tomography (CT) reconstruction imaging technology is an important development direction in the field of CT imaging. The mainstream model of dual-energy CT reconstruction algorithm is the basis material decomposition model, and the projection decomposition is the crucial technique. The projection decomposition algorithm based on projection matching was a general method. With establishing the energy spectrum lookup table, we can obtain the stable solution by the least squares matching method. But the computation cost will increase dramatically when size of lookup table enlarges and it will slow down the computer. In this paper, an acceleration algorithm based on projection matching is proposed. The proposed algorithm makes use of linear equations and plane equations to fit the lookup table data, so that the projection value of the decomposition coefficients can be calculated quickly. As the result of simulation experiment, the acceleration algorithm can greatly shorten the running time of the program to get the stable and correct solution.
The requirement for unconstrained monitoring of heartbeat during sleep is increasing, but the current detection devices can not meet the requirements of convenience and accuracy. This study designed an unconstrained ballistocardiogram (BCG) detection system using acceleration sensor and developed a heart rate extraction algorithm. BCG is a directional signal which is stronger and less affected by respiratory movements along spine direction than in other directions. In order to measure the BCG signal along spine direction during sleep, a 3-axis acceleration sensor was fixed on the bed to collect the vibration signals caused by heartbeat. An approximate frequency range was firstly assumed by frequency analysis to the BCG signals and segmental filtering was conducted to the original vibration signals within the frequency range. Secondly, to identify the true BCG waveform, the accurate frequency band was obtained by comparison with the theoretical waveform. The J waves were detected by BCG energy waveform and an adaptive threshold method was proposed to extract heart rates by using the information of both amplitude and period. The accuracy and robustness of the BCG detection system proposed and the algorithm developed in this study were confirmed by comparison with electrocardiogram (ECG). The test results of 30 subjects showed a high average accuracy of 99.21% to demonstrate the feasibility of the unconstrained BCG detection method based on vibration acceleration.
In order to reduce the impact caused by the contact between the foot and the ground when wearing the lower extremity exoskeleton under the condition of high load, this paper proposed an exoskeleton foot mechanism for improving the foot comfort, and optimized the key index of its influence on the comfort. Firstly, the physical model of foot mechanism was established based on the characteristics of foot stress in gait period, and then the mathematical model of vibration was abstracted. The correctness of the model was verified by the finite element analysis software ANSYS. Then, this paper analyzed the influence of vibration parameters on absolute transmissibility based on vibration mathematical model, and optimized vibration parameters with MATLAB genetic algorithm toolbox. Finally, this paper took white noise to simulate the road elevation as the vibration input, and used the visual simulation tool Simulink in MATLAB and the vibration equation to construct the acceleration simulation model, and then calculated the vibration weighted root mean square acceleration value of the foot. The results of this study show that this foot comfort mechanism can meet the comfort indexes of vibration absorption and plantar pressure, and this paper provides a relatively complete method for the design of exoskeleton foot mechanism, which has reference significance for the design of other exoskeleton foot and ankle joint rehabilitation mechanism.
With the rapid improvement of the perception and computing capacity of mobile devices such as smart phones, human activity recognition using mobile devices as the carrier has been a new research hot-spot. The inertial information collected by the acceleration sensor in the smart mobile device is used for human activity recognition. Compared with the common computer vision recognition, it has the following advantages: convenience, low cost, and better reflection of the essence of human motion. Based on the WISDM data set collected by smart phones, the inertial navigation information and the deep learning algorithm-convolutional neural network (CNN) were adopted to build a human activity recognition model in this paper. The K nearest neighbor algorithm (KNN) and the random forest algorithm were compared with the CNN network in the recognition accuracy to evaluate the performance of the CNN network. The classification accuracy of CNN model reached 92.73%, which was much higher than KNN and random forest. Experimental results show that the CNN algorithm model can achieve more accurate human activity recognition and has broad application prospects in predicting and promoting human health.