This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,qua...This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,quadriceps femoris(vastus medialis and rectus femoris),hamstring/long head of the biceps femoris,gastrocnemius medialis,rectus abdominal,and erector spinae when using the downward breaststroke kick technique.We find that when this sample of swimmers utilized the downward breaststroke kick,max speed and velocity per stroke increased,measured by 12,788 EMG samples,where the results are highly correlated to duration of the aerodynamic buoyant force in breaststroke kick technique.The increases in performance observed from measuring the world class elite swimmers is highly correlated to the duration of the kick aerodynamic buoyant force.Among this sample of elite swimmers,the longer a swimmer demonstrates a buoyant force breaststroke kick,the lower the time in a 100 breaststroke.展开更多
As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts h...As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.展开更多
文摘This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,quadriceps femoris(vastus medialis and rectus femoris),hamstring/long head of the biceps femoris,gastrocnemius medialis,rectus abdominal,and erector spinae when using the downward breaststroke kick technique.We find that when this sample of swimmers utilized the downward breaststroke kick,max speed and velocity per stroke increased,measured by 12,788 EMG samples,where the results are highly correlated to duration of the aerodynamic buoyant force in breaststroke kick technique.The increases in performance observed from measuring the world class elite swimmers is highly correlated to the duration of the kick aerodynamic buoyant force.Among this sample of elite swimmers,the longer a swimmer demonstrates a buoyant force breaststroke kick,the lower the time in a 100 breaststroke.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea,funded by the Ministry of Education(Nos.NRF2017R1A6A1A03015496,RS-2023-00249555).
文摘As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.
