Individualized Generation of Appropriate Assistance Timings for Active Lower Limb Robots via Machine Learning [post]

Guowei Huang, Zikang Zhou, Binghong Liang, Jinjin Nong, Youwei Liu, Longhan Xie
2020 unpublished
Background: Obtaining appropriate assistance timings for individual users of active lower limb assistant robots (ALLARs) is one of the major challenges that limit the practical application of robots since very small assistance timing errors greatly affect the robot's assistance effect. However, neither theoretical nor experimental methods can currently generate appropriate assistance timings due to their respective availability or accuracy limitations. Method: In this paper, we proposed a new
more » ... we proposed a new method to generate appropriate assistance timings for individual users of ALLARs via machine learning. The method has the accuracy of theoretical methods and the availability of experimental methods. We established a database of ten static physiological parameters, three dynamic parameters, and theoretical appropriate assistance timings, and mapped the static physiological parameters and the dynamic parameters to the theoretical assistance timings using multilayer neuron networks. Fold-cross validation and determination efficient were used to test the fit of the model. The root mean square error between generated values and true values of each subject was compared to that between the mean of the sample and the true values of each subject to evaluate the data accuracy of our method. We also set ±2% error as the boundary of the practical accuracy and compared the practical accuracy when using our method to that when using the mean generally. Result: The model achieved a small standard deviation of the square root error in the 10-fold cross-validation experiment and a large determination coefficient. We reduced the data error of starting and ending assistance timing from 0.0265 and 0.0172 to 0.014±0.000429 and 0.0079±0.000875, respectively, and improved the practical accuracy of starting and ending assistance timing from 54.93% and 75.49% to 89.54% and 99.95%, respectively.Conclusion: The proposed method can generate an appropriate assistance timings for different users of ALLARs walking at different speeds. Moreover, a new reference for ending assistance timings is provided and the database can be used as a reference for futer research. The practical effect of the method will be tested in future work.
doi:10.21203/ fatcat:bqzztjuqavhbbix6owpnax4wby