The foot drop functional electrical stimulation (FES) system consisting of various sensors has been widely applied to the disease of the foot drop. However, the current system is limited to the research on walking on the ground and ignores other important actions of foot in one's daily life, such as walking up and down the stairs, squatting and lying down, etc. In this work, we applied the dual axis angle sensor to the system of the foot drop FES for the first time. Such a system can not only stimulate the foot drop during normal walking, but also identify squatting, sitting, and lying down etc. and furthermore, the system can switch off automatically. In the meanwhile, it can also detect falls and other dangerous actions. The accuracy of our system can achieve 100%, 81.9%, 95.8%, 99.0% and 66.9% for normal walking, sitting-standing, walking up the stairs, walking down the stairs and squatting-standing respectively.
Adult ; Biosensing Techniques ; instrumentation ; methods ; Electric Stimulation ; instrumentation ; methods ; Equipment Design ; Female ; Foot Deformities, Acquired ; therapy ; Humans ; Male ; Middle Aged ; Young Adult

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Objective:To establish UPLC specific chromatogram method and content determination method of Dryopteridis Crassirhizomatis Rhizoma; To comprehensively evaluate the Dryopteridis Crassirhizomatis Rhizoma from different producing areas.Methods:UPLC method was used to establish the specific chromatogram of 15 batches of Dryopteridis Crassirhizomatis Rhizoma from different producing areas. The quality was evaluated by cluster analysis and partial least square analysis. The content of total flavonoids was determined by ultraviolet spectrophotometry.Results:The established specific chromatogram identified a total of 21 common peaks, and identified 4 components through comparison with control samples. The clustering and partial least squares analysis showed that the samples from Anshan City in Liaoning Province, Yanbian Korean Autonomous Prefecture in Jilin Province, Jiamusi City in Heilongjiang Province, Jilin City in Jilin Province had a certain consistency. The results of total flavone content determination showed that the content of samples from different producing areas was different.Conclusion:The established UPLC characteristic chromatogram and content determination method of total flavone content of Dryopteridis Crassirhizomatis Rhizoma can reflect the quality of Dryopteridis Crassirhizomatis Rhizoma in different places. The quality of samples from different origins is similar, but the contents of the three producing areas fluctuated greatly.

OBJECTIVE：To esta blish a UPLC fingerprint of Pyrrosia petiolosa from southwest China ，and to determine the contents of 4 kinds of phenolic acids （neochlorogenic acid ，caffeic acid ，chlorogenic acid and cryptochlorogenic acid ）. METHODS：The determination was performed on Waters Cortecs T 3 C18 column（100 mm×2.1 mm，1.6 μm）with mobile phase consisted of methanol- 0.1％ phosphoric acid （gradient elution ）at the flow rate of 0.35 mL/min. The detection wavelength was set at 326 nm. The column temperature was 30 ℃，and injection volume was 1 μL. UPLC method was used to establish the UPLC fingerprint of P. petiolosa in combination with the Similarity Evaluation System of TCM Chromatographic Fingerprints （2012 edition）. Cluster analysis and principle component analysis （PCA）were performed by using SPSS 20.0 software. The contents of 4 kinds of phenolic acids in 20 batches of P. petiolosa were determined by external standard method. RESULTS ：There were 9 common peaks for the UPLC fingerprint of P. petiolosa . Peaks 1，3，4，5 and 9 were identified as neochlorogenic acid ，caffeic acid，chlorogenic acid ，cryptochlorogenic acid and isochlorogenic acid C ，respectively. RSDs of the relative retention time of each peak in different batches of P. petiolosa were 0-0.68％，and the RSDs of the relative peak area were 0-62.35％. The similarities between the fingerprint of 20 batches of medicinal materials and the control chromatogram were not less than 0.990. The result of cluster analysis showed that P. petiolosa from different regions could be sorted into three species. Results of PCA showed the differences among P. petiolosa from different regions. The linear range of neochlorogenic acid ，caffeic acid ，chlorogenic acid and cryptochlorogenic acid were 0.61-61.41，0.18-17.60，2.00-200.11，0.62-61.51 μ g/mL （R2＞0.999 9）. RSDs of precision ， reproducibility and stability tests were all lower than 2.00％. The recoveries were 96.23％-98.17％（RSD＝0.96％-2.28％， n＝6）. Among 20 batches of samples ，the contents of above 4 kinds of phenolic acids were 0.385 3-1.891 9，0.018 0-0.129 5，2.569 5-10.676 0，0.563.5-1.860 5 mg/g. CONCLUSIONS ： The established UPL C fingerprint could reflect the main chemical constituents of P. pedunculata . Phenolic acids could be used as the main evaluation indexes for the quality of P. petiolosa . The quality order of P. petiolosa from southwest China was Chongqing product＞Sichuan product ＞Guizhou product.

OBJECTIVE：To provide reference for the identification of Chebulae Fructus and Chebulae Fructus Immaturus . METHODS：UPLC method was adopted. The determination was performed on Waters Cortecs T 3 C18 column with mobile phase consisted of acetonitrile- 0.2％ phosphoric acid solution （gradient elution ）at the flow rate of 0.35 mL/min. The column temperature was 30 ℃，and the detection wavelength was set at 270 nm. The sample size was 1 μL. Using gallic acid as reference，UPLC fingerprints of 17 batches of Chebulae Fructus and 14 batches of Chebulae Fructus Immaturus were established and their similarity was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System （2012 edition）. By comparing substance control ， UV absorption spectrum and related literaturs ，common peaks were identified. PCA and PLS-DA were performed by using SPSS 20.0 and SIMCA 14.1 software. The contents of main difference components in Chebulae Fructus and Chebulae Fructus Immaturus were determined by above UPLC method and compared. RESULTS ：There were 8 common peaks in UPLC fingerprint of Chebulae Fructus and Chebulae Fructus Immaturus ，i.e. chebulic acid （peak 1），gallic acid （peak 2），punicalagin A （peak 3），punicalagin B （peak 4），corilagin（peak 6），chebulagic acid （peak 7）and chebulinic acid （peak 8）. The similarities of 17 batches of Chebulae Fructus were from 0.92 to 0.99，while 14 batches of Chebulae Fructus Immaturus were all above 0.99. The similarity of control fingerprint between Chebulae Fructus and Chebulae Fructus Immaturus was 0.909. PCA demonstrated the differences between Chebulae Fructus and Chebulae Fructus Immaturus . The results of PLS-DA were consistent with those of PCA ，and the variable importance in projection （VIP）values of peak 5，4，7，3 and 2 were above 1 in the PLS-DA model. In 31 batches of samples ，the contents of gallic acid （peak 2），punicalagin A（peak 3），punicalagin B （peak 4）and chebulagic acid （peak 7）were 2.63-10.31， 5.37-44.63，8.02-60.77，44.07-162.98 mg/g；RSDs were 40.14％， 47.91％ ，53.97％ ，36.22％（n＝31）. There was statistical significance in the differences of the mentioned 4 components between Chebulae Fructus and Chebulae Fructus Immaturus 719412818@qq.com （P＜0.05）. CONCLUSIONS ：There are significant differences between Chebulae Fructus and Chebulae Fructus Immaturus gallic acid ，punicalagin A ，punicalagin B and chebulagic acid are the main difference components for identification.