|Place of Conferral||中国科学院自动化研究所|
With the cross-impact of various factors such as economic and social development, increasing population ageing, and expansion of transportation scale, traumatic fracture diseases, which occur frequently, are increasingly becoming a prominent problem affecting human life and health. The lower limbs are the common and prone parts of fractures because they bear the body's own weight and are responsible for basic movements. The treatment of lower limb fractures consists of reduction surgery and postoperative rehabilitation. Limited by the surgeon's experience and intraoperative equipment, traditional fracture surgery, with drawbacks of large trauma, high surgical risks and hard to popularization, is prone to lots of risks such as inaccurate reduction and secondary infection. At the same time, the two processes of fracture reduction and fracture rehabilitation are severely disconnected. The fracture rehabilitation process lacks effective rehabilitation strategies and quantitative assessment, which constantly causes severe problems, like aimless and long periods of rehabilitation, even nonunion after fractures. The robotic system integrated with accurate fracture reduction function and quantitative assessment function is an effective solution to the above problems, which has become a research hotspot in both academia and industry. At present, the rehabilitation process after fracture still faces a large number of challenges, such as the recognition of motion intentions in active rehabilitation training and the determination of criteria in quantitative rehabilitation. Therefore, supported by the National Key Research and Development project, this paper focuses on three key problems, including motion intention recognition algorithm based on EEG data, motion intention recognition algorithm based on sEMG data and the quantitative assessment of rehabilitation training based on the requirements of the robotic system and actual clinical needs. The main research contents and contributions of this paper are as follows:
Two intention recognition models based on the spatio-temporal and the spatio-temporal-spectral features of EEG data are innovatively constructed, separately. Extensive evaluations based on public dataset show that the proposed models outperform existing methods. On the one hand, EEG data are converted into image sequences while preserving the spatial features by projecting EEG data of all channels recorded at the same time onto a 2-D surface. Next, the Convolutional Long Short-Term Memory network is used to encode spatio-temporal features and generate a better representation from the obtained image sequence. The convolutional layer is used to further extract features, and softmax is used for classification. On the other hand, the complex Morlet wave convolution is used to convert the EEG signals of multiple trials into a time-frequency representation that reflects the time and spectral features. Next, the convolutional neural network combined with the attention mechanism is designed to extract the features of each channel and give the key channels higher attention weights. Finally, the attention weight and extracted features are aggregated for classification. Extensive evaluations based on public dataset show that the proposed models outperform existing methods.
Due to EEG data are difficult to recognize the multiple types of lower limb movement intentions, four types of lower limb movements are designed for patients with lower limb fractures. A lower limb motion intention recognition model based on sEMG data is proposed. For four types of lower limb movements, the sEMG data of key skeletal muscles of four patients with lower limb fractures are collected. Experiments are carried out from the four perspectives, such as data segmentation length, single feature selection, multiple feature combination, and different kinds of classifier. When window length is 200ms, and zero-crossing, waveform length, wavelet transform are combined to form the feature vector, using BP neural network as the classifier, the highest motion intention recognition rate can be obtained. The recognition rates of lower limb movement intention for four patients are 95.27%, 96.28%, 99.73%, 98.16%, respectively.
An sEMG-based method for objective division of rehabilitation stages and lower limb muscle strength assessment is proposed to meet the needs of rehabilitation assessment after lower limb fractures. To address the rehabilitation stage division problem, 50ms data window is designed. Mean absolute value, root mean square and wavelet transform are chosen to form feature vectors. The final classification accuracy of the support vector machine algorithm is 91.81\%. sEMG data are used to indicate the muscle strength of the lower limbs, and the data window is 25ms. The mean absolute value and root mean square features are extracted to compare the muscle strength of the healthy side and the affected side. The result shows that the muscle strength of the affected side in most patients is significantly weaker than that of the healthy side. Furthermore, the patient's muscle strength of the affected side is tracked along with the progress of the rehabilitation process based on this method. The result shows that the strength of the peroneus longus muscle on the affected side increases to a certain extent and develops in a positive direction after one month of rehabilitation training.
|方志杰. 面向机器人辅助下肢骨折康复的运动意图识别与量化评价[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2021.|
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