BACKGROUND:Gait movements are one of the important characteristics exhibited by athletes during exercise,reflecting their physical condition and athletic ability.In a state of fatigue,athletes may exhibit abnormal gait movements,such as reduced stride and body shaking,which can cause harm to their bodies.OBJECTIVE:To promote technological progress in the field of sports science by applying advanced algorithms and data analysis techniques to the training practice of athletes,so as to further improve the recognition accuracy of gait movements under sports fatigue.METHODS:A gait recognition method for athletes in fatigue state was based on fish swarm algorithm.By utilizing the normalized autocorrelation function and the principle of motion energy distribution,a single cycle gait energy map of athletes was obtained.Singular value decomposition was used to transform the image to highlight visual differences,generating a gait energy map of athletes.A convolutional neural network was used to construct a gait action recognition model,and the parameters of the model were solved using the fish swarm algorithm to improve the accuracy and efficiency of fatigue gait action recognition.RESULTS AND CONCLUSION:(1)The fish swarm algorithm had a small loss value in gait action recognition,and could accurately and quickly identify the gait actions of athletes,and dynamically monitor their physical fatigue.(2)The research on fatigue gait recognition of athletes based on fish swarm algorithm could effectively identify the gait movements of athletes in fatigue state and achieve accurate capture of subtle gait changes.(3)The system stability of this method is good,which can reduce the volatility of experimental test results and improve recognition efficiency,can more effectively manage sports fatigue and prevent sports injuries.In addition,when the gait characteristics of normal people change significantly,the system can give an early warning,indicating that the individual may be in a state of fatigue and need to rest or adjust the intensity of activity.

BACKGROUND:Gait movements are one of the important characteristics exhibited by athletes during exercise,reflecting their physical condition and athletic ability.In a state of fatigue,athletes may exhibit abnormal gait movements,such as reduced stride and body shaking,which can cause harm to their bodies.OBJECTIVE:To promote technological progress in the field of sports science by applying advanced algorithms and data analysis techniques to the training practice of athletes,so as to further improve the recognition accuracy of gait movements under sports fatigue.METHODS:A gait recognition method for athletes in fatigue state was based on fish swarm algorithm.By utilizing the normalized autocorrelation function and the principle of motion energy distribution,a single cycle gait energy map of athletes was obtained.Singular value decomposition was used to transform the image to highlight visual differences,generating a gait energy map of athletes.A convolutional neural network was used to construct a gait action recognition model,and the parameters of the model were solved using the fish swarm algorithm to improve the accuracy and efficiency of fatigue gait action recognition.RESULTS AND CONCLUSION:(1)The fish swarm algorithm had a small loss value in gait action recognition,and could accurately and quickly identify the gait actions of athletes,and dynamically monitor their physical fatigue.(2)The research on fatigue gait recognition of athletes based on fish swarm algorithm could effectively identify the gait movements of athletes in fatigue state and achieve accurate capture of subtle gait changes.(3)The system stability of this method is good,which can reduce the volatility of experimental test results and improve recognition efficiency,can more effectively manage sports fatigue and prevent sports injuries.In addition,when the gait characteristics of normal people change significantly,the system can give an early warning,indicating that the individual may be in a state of fatigue and need to rest or adjust the intensity of activity.

Objective:To investigate the factors contributing to frailty in very elderly patients with multimorbidity.Methods:This cross-sectional study enrolled 119 very elderly patients with multimorbidity who were hospitalized in the Department of Geriatrics of Huadong Hospital Affiliated to Fudan University from August 2022 to March 2023.The study aimed to understand the basic status of multimorbidity by collecting general information, the number and types of diseases, and frailty status.The subjects were divided into frail and non-frail groups through comprehensive geriatric assessment.Various factors including gender, age, Tinetti balance gait score, risk of sarcopenia, dementia, depression, risk of deep vein thrombosis, dysphagia, comorbidity index, medication count, Basic Activities of Daily Living(BADL)score, Instrumental Activities of Daily Living(IADL)score, Nutritional Risk Screening 2002(NRS-2002)score, Norton pressure injury risk assessment score, and Social Support Rating Scale(SSRS)score were compared.The correlation between each factor and the occurrence of frailty was analyzed using univariate analysis and multivariate Logistic regression analysis.Results:A total of 119 elderly inpatients with multimorbidity, with an average age of 90.8±5.9 years old, were included in the study.The incidence of frailty was 68.9%(82 cases).Univariate analysis revealed significant statistical differences between the frail group and the non-frail group in various factors including age( t=-3.131, P=0.002), Tinetti score( Z=-5.544, P<0.001), risk of sarcopenia( χ2=39.205, P<0.001), dysphagia( χ2=5.937, P=0.015), Charlson comorbidity index( Z=-2.565, P=0.010), medication count( Z=-3.325, P<0.001), BADL( Z=-5.871, P<0.001), IADL( Z=-5.062, P<0.001), Norton score( Z=-5.922, P<0.001), and SSRS social support( Z=-2.637, P=0.008).Multivariate logistic regression analysis showed that the Tinetti score( OR=0.843, 95% CI: 0.737-0.966, P=0.014), decreased muscle strength( OR=11.226, 95% CI: 2.157-58.432, P=0.004), sarcopenia( OR=18.084, 95% CI: 2.041-106.211, P=0.009), Norton score( OR=0.462, 95% CI: 0.254-0.838, P=0.011), and medication count( OR=1.153, 95% CI: 1.000-1.329, P=0.049)were independently associated with frailty. Conclusions:In very elderly patients with multimorbidities, the occurrence of frailty is notably increased.Frailty is linked to multiple risks including falls, muscle weakness/sarcopenia, pressure ulcer risk, and polypharmacy, and these risks are independent of other factors.

【Objective】 To explore a method to accurately identify the specificity of alloantibodies or autoantibodies in autoimmune hemolytic anemia (AIHA)patients with both warm and cold antibodies, so as to provide guidance for the selection of blood components. 【Methods】 Blood samples of AIHA patients with both warm and cold antibodies were screened by the direct antiglobulin testing (DAT). The plasma of patients were treated with dilution or adsorption method and the erythrocyte was dispersed for specificity identification of alloantibodies or autoantibodies.According to the results of antibody identification, appropriate phenotype of red blood cells(RBCs) were transfused to patients, and the incidence of adverse reactions and efficacy of transfusion were observed. 【Results】 Alloantibodies or specific autoantibodies were detected in serum or elution in 14 of the 16 patients. 10 patients underwent blood transfusion during hospitalization, and all of them received RBCs with the same or compatible ABO/Rh (D) type as the patients and without any reaction to the alloantibodies and specific warm autoantibodies. No hemolytic reaction occurred, and anemia symptoms were improved after blood transfusion. 【Conclusion】 The selection of appropriate methods could eliminate the influence of autoantibodies on the identification of alloantibodies in AIHA patients with both warm and cold antibodies. Therefore, the selection of blood from compatible donors for transfusion could effectively avoid the occurrence of hemolytic reaction.