Continuous angular position estimation of human ankle during unconstrained locomotion

• Published in: Biomedical signal processing and control Vol. 60; p. 101968
• Main Authors: Gupta, Rohit, Dhindsa, Inderjeet Singh, Agarwal, Ravinder
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2020
• Subjects: Angular position; NARX; Prediction Model; Prostheses; SEMG; Prostheses; Angular position; SEMG; Prediction Model; NARX
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2020.101968

•The current research work presents an ankle joint angle estimation model using SEMG signal of only two lower limb muscles along with knee joint angle signal.•This hybrid approach blends the advantages of both SEMG (ability to predict the movement beforehand) and knee joint angle (robustness) signals.•The estimation of joint angle takes place in continuous manner which make it suitable for prosthesis control.•The proposed model has been validated for five daily life locomotion, Level walk, Stair ascent, Stair descent, Ramp ascent and Ramp descent. Active lower limb prostheses required an efficient user interface and robust control structure for its seamless operation. A nonlinear autoregressive model has been proposed and evaluated to estimate the ankle joint angle continuously. It utilizes surface electromyogram (SEMG) and knee joint angle (KA) signals inputs. The performance of the proposed model has been assessed based on the accuracy and consistency of angle estimation. A dataset of ten subjects acquired for five daily life locomotor activities has been used for model performance evaluation. Also, the contribution of KA signal towards ankle joint angle estimation has been examined. The average angle estimation error over the subjects has been found in the range of 2.38 ± 0.78° to 5.45 ± 1.98° for various dynamic activities. The contribution of KA signal has been found significant (One-way ANOVA, p-value<0.05) as it improves the model accuracy and consistency by 52% and 36%, respectively. The proposed model provides an opportunity for direct control of ankle-foot prostheses by continuously predicting the ankle joint angle using SEMG and KA signal. The model’s performance proves its applicability for ankle joint angular orientation estimation for active prostheses, orthoses, and lower limb rehabilitation.