Individualized reference gait patterns for lower limb rehabilitation robots can greatly improve the effectiveness of rehabilitation. However, previous methods can only generate customized gait patterns at several fixed discrete walking speeds and generating gaits at continuously varying speeds and stride lengths remains unsolved. This work proposes an individualized gait pattern generation method based on a recurrent neural network (RNN), which is proficient in series modeling. We collected the largest gait data set of this kind, which consists of 4,425 gait patterns from 137 subjects. Using this data set, we trained an RNN to create a function mapping from body parameters and gait parameters to a gait pattern. The experimental results indicate that our model is able to generate gait patterns at continuously varying walking speeds and stride lengths while also reducing the errors in the ankle, knee, and hip measurements by 12.83%, 20.95%, and 28.25%, respectively, compared to previous state-of-the-art method.
Purpose Stroke patients often suffer from strephenopodia because of high muscle tension or muscle spasms, which seriously affect their walking ability and rehabilitation. During the treatment of strephenopodia, there are practical demands for convenient, automatic, and quantitative assessments of the angle of strephenopodia. However, existing strephenopodia detection methods, including traditional clinical gait analysis, gait video analysis and plantar pressure systems, suffer from object obstruction or require complex setups. In this paper, we proposed a novel methodology for automatically predicting the angles of strephenopodia based on a gait analysis system using machine learning methods.Methods Plantar pressure distribution data from thirty healthy participants were recorded during walking on the Zebris FDM-THM instrumented treadmill and were processed to generate 15 gait features. The right ankle angles on the coronal plane were measured by the Vicon system to provide a detailed description and explanation of strephenopodia walking. Three machine learning methods were implemented to build stochastic function mapping from gait features to strephenopodia angles.Results This study showed good reliability and precision prediction of the angle of strephenopodia [determination coefficient (R2)\(\ge\)0.80]. Gaussian process regression (GPR) exhibited the best regression performance [R2 = 0.93, mean root-mean-square error (RMSE) = 0.67].Conclusion The study results showed that this strephenopodia-detection method is not only convenient to implement but also has high accuracy and outperforms previous reports. Measurements derived from the gait analysis system are proper estimators of the angle of strephenopodia and should be considered to improve diagnosis and assessment of the stroke population.
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 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.
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