Objective To explore an improved method of surgical operation for reposition of the articular surface with Type Ⅲ Pilon fractures. Methods From January 1999 to December 2005, 20 patients (22 sides) with Type Ⅲ Pilon fractures were treated with the delayed open reduction and the internal fixation, which took the superior articular surface of the talus as a templet so as to reposition the lower articular surface of the tibia, strengthen the bone transplantation, fasten the internal fixation, and make an early functional exercise possible. Complete data were obtained from 16 of the patients with 18 sides (13 males,15 sides; 3 females, 3 sides; age, 14-48 years). The injury due to a fallingaccident was found in 12 patients (14 sides), and due to a traffic accident in 4patients (4 sides). Results The healing of the first intention was achieved in 14 sides, the delayed healing in 3 sides, and the infection in 1 side. The follow-up of all the 16 patients for 971 months (average, 22 months) including the X-ray examinations revealed that no screw for the internal fixation entering the articular cavity. According to the Teeny’s judging standards of radiology evaluating the result of the surgery for Pilon fractures, the anatomical reduction of the related articular surface was found in 77.8% of the sides (14/18) and thehealing of the first intention (stage Ⅰ) in 94.4% (17/18). According to the Mazur’s criteria, an excellent result was obtained in 5 sides, good in 7, fair in 5, and poor in 1. The excellent and good result was 66.7%. Conclusion Propermanagement of the injured soft tissues, prompt recovery of the tibial distant plateau height, and accurate reposition of the articular surface, enough transplant bone for the solid support, b internal fixation for the distant tibial anatomical structure, and early functional exercise are the key points to the successful operation.
In this study, surface electromyography (sEMG) of the lower limbs of cerebral-palsy (CP) subjects in gait cycle was recorded and its parameters of gait cycle characters were analyzed to assess their clinical severity. Three algorithms, including integrated profile (IP), sample-entropy (SampEN) and smooth nonlinear energy operator (SNEO) algorithm, were applied to calculate the duration of walking sEMG segments in simulated SEMG signals. After that, the efficiency and accuracy were compared among these three algorithms. SNEO was then selected as the optimal algorithm among the three algorithms and employed for real sEMG signal processing of CP subjects. The results indicated that there was no significant difference in the accuracy of sEMG segement detection for the three algorithms. However, the computation speed of SNEO algorithm was much faster than those of the others and thus it was a suitable algorithm for detecting walking sEMG segments of CP subjects. In addition, the positive correlation was found between the clinical severity and the mean duration of walking sEMG segments in CP subjects. The results indicated that there was a significant difference in the three groups of CP subjects with different levels of severity. Our findings showed that the mean duration of walking sEMG segments could be considered as an assistant index to evaluate the clinical severity of CP subjects.
Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.
Objective To review the current researches of scaffold materials for skeletal muscle tissue engineering, to predict the development trend of scaffold materials in skeletal muscle tissue engineering in future. Methods The related l iterature on skeletal muscle tissue engineering, involving categories and properties of scaffold materials, preparative techniqueand biocompatibil ity, was summarized and analyzed. Results Various scaffold materials were used in skeletal muscle tissue engineering, including inorganic biomaterials, biodegradable polymers, natural biomaterial, and biomedical composites. According to different needs of the research, various scaffolds were prepared due to different biomaterials, preparative techniques, and surface modifications. Conclusion The development trend and perspective of skeletal muscle tissue engineering are the use of composite materials, and the preparation of composite scaffolds and surface modification according to the specific functions of scaffolds.
Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
In order to conduct surface monitoring of the three-dimensional spine morphology of the human body in daily life, a spine morphology measuring method using "single camera, multi-view" to construct stereo vision is proposed. The images of the back of the human body with landmarks of spinous process are captured from multiple angles by moving a single camera, and based on the "Zhang Zhengyou calibration method" and the triangulation principle of binocular stereo vision, the spatial conversion matrices corresponding to each other between all images and the 3D coordinates of the landmarks are calculated. Then the spine evaluation angle used to evaluate the spine morphology is further calculated. The tests’ results showed that the spine evaluation angle error of this method is within ±3°, and the correlation between the results and the X-ray film Cobb angles is 0.871. The visual detection algorithm used in this paper is non-radioactive, and because only one camera is used in the measurement process and there is no need to pre-set the camera's shooting pose, the operation is simple. The research results of this article can be used in a mobile phone-based intelligent detection system, which will be suitable for the group survey of scoliosis in communities, schools, families and other occasions, as well as for the long-term follow-up of confirmed patients. This will provide a reference for doctors to diagnose the condition, predict the development trend of the condition, and formulate treatment plans.
This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects’ brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.
This paper aims to propose a noninvasive radiotherapy patient positioning system based on structured light surface imaging, and evaluate its clinical feasibility. First, structured light sensors were used to obtain the panoramic point clouds during radiotherapy positioning in real time. The fusion of different point clouds and coordinate transformation were realized based on optical calibration and pose estimation, and the body surface was segmented referring to the preset region of interest (ROI). Then, the global-local registration of cross-source point cloud was achieved based on algorithms such as random sample consensus (RANSAC) and iterative closest point (ICP), to calculate 6 degrees of freedom (DoF) positioning deviation and provide guidance for the correction of couch shifts. The evaluation of the system was carried out based on a rigid adult phantom and volunteers’ body, which included positioning error, correlation analysis, and receiver operating characteristic (ROC) analysis. Using Cone Beam CT (CBCT) as the gold standard, the maximum translation and rotation errors of this system were (1.5 ± 0.9) mm along Vrt direction (chest) and (0.7 ± 0.3) ° along Pitch direction (head and neck). The Pearson correlation coefficient between results of system outputs and CBCT verification distributed in an interval of [0.80, 0.84]. Results of ROC analysis showed that the translational and rotational AUC values were 0.82 and 0.85, respectively. In the 4D freedom accuracy test on the human body of volunteers, the maximum translation and rotation errors were (2.6 ± 1.1) mm (Vrt direction, chest and abdomen) and (0.8 ± 0.4)° (Rtn direction, chest and abdomen) respectively. In summary, the positioning system based on structured light body surface imaging proposed in this article can ensure positioning accuracy without surface markers and additional doses, and is feasible for clinical application.
To review the structure and function of the calcified cartilage zone and its role in the pathogenesis of osteoarthritis (OA). Methods Recent l iterature about calcified zone was reviewed and analyzed in terms of architecture, composition, biomechanics, and biological function. Results Calcified zone has particular structure and material properties, and functions as a semi permeable membrane; chondrocytes in the calcified zone retain some characteristics of growth plate cells, which play a crucial role in cartilage function maintenance and pathogenesis of OA. Therefore, reconstructionof the calcified zone at osteochondral conjunction has become one of the hot research in the fields of interface tissue engineering. Conclusion It is necessary to pay more attention to calcified cartilage zone, which is important for both the treatment of OA and the preparation of tissue engineered osteochondral composite.
Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern (wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.