A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 812 - 821
• Main Authors: Mobarak, Rami, Tigrini, Andrea, Verdini, Federica, Al-Timemy, Ali H., Fioretti, Sandro, Burattini, Laura, Mengarelli, Alessandro
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ankle; ankle joint; Ankle Joint - physiology; Artificial neural networks; Biomechanical Phenomena; Electromyography; Electromyography - methods; Estimation; Feature extraction; Gait - physiology; Hip; hip joint; Humans; Joints (anatomy); Kinematics; kinematics estimation; Knee Joint; least squares-support vector machine regression; Lower Extremity; Muscle, Skeletal - physiology; Muscles; Myoelectricity; Neural networks; Prosthetics; Regression analysis; Regression models; Reproducibility of Results; Support vector machines; Surface electromyography; Thigh; Trajectory; Walking; Walking - physiology
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3364976

In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking task. For each subject, the angular kinematics of hip and ankle joints were synchronously recorded with sEMG signal for a total of 288 gait cycles. Two feature sets were extracted from sEMG signals, i.e. time domain (TD) and wavelet (WT) and compared to have a compromise between the reliability and computational capacity, they were used for feeding three regression models, i.e. Artificial Neural Networks, Random Forest, and Least Squares - Support Vector Machine (LS-SVM). BF together with LS-SVM provided the best ankle angle estimation in both TD and WT domains (RMSE < 5.6 deg). The inclusion of Hip joint trajectory significantly enhanced the regression performances of the model (RMSE < 4.5 deg). Results showed the feasibility of estimating the ankle trajectory using only proximal and limited information from the lower limb which would maximize a potential transfemoral amputee user's comfortability while facing the challenge of having a small amount of information thus requiring robust data-driven models. These findings represent a significant step towards the development of a minimal setup useful for the control design of ankle active prosthetics and rehabilitative solutions.