A Random Forest Approach for Continuous Prediction of Joint Angles and Moments During Walking: An Implication for Controlling Active Knee-Ankle Prostheses/Orthoses

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

“…The accuracy of our model for ankle joint dorsi/plantar exion angle prediction (3.61 and 7.72 for intra and inter-subject examinations) was comparable to previous studies with an RMSE of 1.9 to 9.75 degrees [45,46,48,50,55]. Other research groups achieved higher accuracy by combining wearable sensors' data with machine learning techniques for joint kinematics prediction [11][12][13][14][15][16][17]19]. The better performance of this approach (IMUs + ML model) provided lower estimation errors in previous studies, especially in the intra-subject examinations with an RMSE ranging from 1.72 to 3.58 degrees in hip exion/extension [11,14,15], from 2.21 to 3.96 in knee exion/extension [11,12,14,15] and from 1.81 to 3.58 degrees in ankle dorsi/plantar exion angle [11,12,14,15].…”

“…Other research groups achieved higher accuracy by combining wearable sensors' data with machine learning techniques for joint kinematics prediction [11][12][13][14][15][16][17]19]. The better performance of this approach (IMUs + ML model) provided lower estimation errors in previous studies, especially in the intra-subject examinations with an RMSE ranging from 1.72 to 3.58 degrees in hip exion/extension [11,14,15], from 2.21 to 3.96 in knee exion/extension [11,12,14,15] and from 1.81 to 3.58 degrees in ankle dorsi/plantar exion angle [11,12,14,15]. The performance of our RF model in intra-subject examination was in the range of these studies with an average RMSE of 2.26 degrees at the hip, 2.89 for knee, and 3.61 for ankle angles in the sagittal plane.…”

“…While previous studies concentrated on joint range of motion in the sagittal plane, our study additionally included the pelvis in all plane, hip int/ext rotation and abd/add and ankle inv/eversion. Fewer studies were conducted to investigate the performance of different ML models for joint kinetics estimation [12,[20][21][22] compared to joint kinematics. All previous studies would focus on a speci c lower-limb joint kinetics (e.g.…”

“…knee and ankle moments in sagittal plane [12], knee adduction/abduction moment [20], medial and lateral knee contact forces [21], knee exion/extension and adduction/abduction moments [22]), while the present study investigated the prediction accuracy for the pelvis (in three planes of motion), hip (in three planes of motion), knee (in sagittal plane) and ankle (in sagittal and frontal planes) joint moments during gait. The RF model presented in our study achieved RMSE of 0.079 Nm/kg for ankle moment prediction in intra-subject examination which is more accurate compared to previous studies with an RMSE of 0.119 Nm/kg [12]. For the knee exion/extension moment, we had lower accuracy in intra-subject examination compared to other studies (RMSE of 0.102 versus RMSE ranging from 0.042 to 0.068 Nm/kg [12,20]).…”

Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning

“…Artificial neural networks are commonly used to predict lower limb joint angles and moments [40][41][42][43][44], ground reaction forces [45], joint forces or impulses [46][47][48][49] and contact pressures [50][51][52]. On the other hand, support vector machines have also demonstrated promising prediction performance in various regression biomechanical problems, such as electromyography (EMG)-based prediction of lumbosacral joint loads [53] and optical marker-based prediction of lower limb joint angles and moments [54,55], standing out for their substantial generalization ability to unseen datasets [56]. For an analytic review on ML applications in human movement biomechanics, including also unsupervised learning techniques, we refer to the survey of Halilaj et al [57].…”