Estimation of knee and ankle angles during walking using thigh and shank angles. (12th October 2021)

• Record Type: Journal Article
• Authors: Eslamy, Mahdy; Schilling, Arndt F
• Is Part Of: Bioinspiration & biomimetics. Volume 16:Number 6(2021)
• Publication Date: 2021-10-12
• Subjects: estimation of knee angles -- estimation of ankle angles -- gait variable estimations -- wavelets-based joint angle estimation; Biomimetics -- Periodicals; Biomedical materials -- Periodicals; Medical innovations -- Periodicals; Biomedical engineering -- Periodicals; 600
• DOI: 10.1088/1748-3190/ac245f
• Languages: English
• ISSNs: 1748-3182

Abstract

Abstract: Estimation of joints' trajectories is commonly used in human gait analysis, and in the development of motion planners and high-level controllers for prosthetics, orthotics, exoskeletons and humanoids. Human locomotion is the result of the cooperation between leg joints and limbs. This suggests the existence of underlying relationships between them which lead to a harmonic gait. In this study we aimed to estimate knee and ankle trajectories using thigh and shank angles. To do so, an estimation approach was developed that continuously mapped the inputs to the outputs, which did not require switching rules, speed estimation, gait percent identification or look-up tables. The estimation algorithm was based on a nonlinear auto-regressive model with exogenous inputs. The method was then combined with wavelets theory, and then the two were used in a neural network. To evaluate the estimation performance, three scenarios were developed which used only one source of inputs (i.e., only shank angles or only thigh angles). First, knee angles θ k (outputs) were estimated using thigh angles θ th (inputs). Second, ankle angles θ a (outputs) were estimated using thigh angles θ sh (inputs), and third, the ankle angles were estimated using shank angles (inputs). The proposed approach was investigated for 22 subjects at different walking speeds and the leave-one-subject-out procedure was used for training and testing the estimation algorithm. Average root mean square errors were 3.9°–5.3° and 2.1°–2.3° for knee and ankle angles, respectively. Average mean absolute errors (MAEs) MAEs were 3.2°–4° and 1.7°–1.8°, and average correlation coefficients ρ cc were 0.95–0.98 and 0.94–0.96 for knee and ankle angles, respectively. The limitations and strengths of the proposed approach are discussed in detail and the results are compared with several studies.