首页|Research Results from Chaoyang University of Technology Update Understanding of Machine Learning (Predicting Gait Parameters of Leg Movement with sEMG and Accel erometer Using CatBoost Machine Learning)
Research Results from Chaoyang University of Technology Update Understanding of Machine Learning (Predicting Gait Parameters of Leg Movement with sEMG and Accel erometer Using CatBoost Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news originating from Chaoyang Un iversity of Technology by NewsRx correspondents, research stated, "This study ai ms to evaluate leg movement by integrating gait analysis with surface electromyo graphy (sEMG) and accelerometer (ACC) data from the lower limbs." Funders for this research include National Science And Technology Council, Taiwa n. Our news reporters obtained a quote from the research from Chaoyang University o f Technology: "We employed a wireless, self-made, and multi-channel measurement system in combination with commercial GaitUp Physilog®5 shoe-worn inertial sensors to record the walking patterns and muscle activati ons of 17 participants. This approach generated a comprehensive dataset comprisi ng 1452 samples. To accurately predict gait parameters, a machine learning model was developed using features extracted from the sEMG signals of thigh and calf muscles, and ACCs from both legs. The study utilized evaluation metrics includin g accuracy (R2), Pearson correlation coefficient (PCC), root mean squared error (RMSE), mean a bsolute percentage error (MAPE), mean squared error (MSE), and mean absolute err or (MAE) to evaluate the performance of the proposed model. The results highligh ted the superiority of the CatBoost model over alternatives like XGBoost and Dec ision Trees. The CatBoost's average PCCs for 17 temporospatial gait parameters o f the left and right legs are 0.878 ± 0.169 and 0.921 ± 0.047, respectively, wit h MSE of 7.65, RMSE of 1.48, MAE of 1.00, MAPE of 0.03, and Accuracy (R2-Score) of 0.91."
Chaoyang University of TechnologyCybor gsEmerging TechnologiesMachine Learning
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