Machine Learning for User-Dependent Ankle Joint Torque Estimation: An Application of XGBoost

Abstract: Powered exoskeletons are increasingly studied to reduce walking effort. Providing optimal assistance requires user gait data, particularly ankle joint torque, which must be estimated despite available sensor measurements. While precomputed estimations exist for target populations, personalized estimation is preferable. Traditional musculoskeletal models are used, but recent approaches leverage Machine Learning (ML) regressions, such as support vector machines (SVM) or Deep Learning (DL) Recurrent Neural Networks (RNN) such as LSTM-based tools. These models need extensive, high-quality data, outsourced from various sensor types for accurate and robust estimations. Studies show that ankle dynamics vary significantly, closely linked to walking speed and user anthropometrics.We utilize a dataset of 138 healthy individuals, which includes EMG, kinematic, and dynamic leg joint data, as well as walking speed (0.97–1.59 m/s), height (1.68 ± 0.10 m), weight (74 ± 15 kg), age (21–86 years), and sex (65M/73F). By employing the XGBoost ML tool, we propose that an ankle joint torque estimator that integrates angular ankle position, walking speed, and anthropometric data can achieve accurate, robust predictions without the need for muscular data. Predictions are validated against unseen torque trajectories, and the performances of the estimator are compared to recent studies on lower limb joint torque estimation.Bootstrap evaluation of the estimators compared to the calculated dynamic data shows a correlation coefficient R² = 0.981 ± 0.051 (p<0.05) and an RMSE mean square error of 0.070 ± 0.008 Nm/kg (p<0.05). Among all data characteristics, angular position (71%), stride percentage (18%), age (2.7%), walking speed (1.7%), and height (1.2%) had the greatest impact on predictions.These results acknowledge the conclusions of previous work on the non-necessity of EMG data and qualify the contribution of other previous work of taking into account the subject's walking speed and anthropomorphic data with gait kinematic and dynamic data. What's more, these results, based on a more representative and homogeneous dataset than some previous work, suggest that there may be informative redundancies between these anthropomorphic features that the model could ignore to be leaner.In the end, this joint torque estimator integrating gait speed and the subject's anthropomorphic data while dispensing with EMG data, yields customized results that are sufficiently accurate and robust to be used in ankle exoskeleton controllers to help to achieve better gait assistance than average profiles with a few mechanical sensors.

Keywords: Machine Learning, XGBoost, torque estimation, ankle

DOI: 10.54941/ahfe1006701

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