English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To reinstate human-like locomotion by using robotic prosthetics, orthotics or exoskeletons, a main challenge is how to coordinate the motion of these devices with that of the biological limbs. One approach to overcome this challenge is to identify firstly the relationships that exist between the kinematics and kinetics of the lower extremity joints and limbs. In this work we aimed to continuously estimate sagittal plane ankle, knee and hip moments using shank or thigh angles. For this purpose, neural network and wavelets were used in a nonlinear auto-regressive model with exogenous inputs. This approach circumvented the need for switching rules or intermediate parameters. To assess the performance of the estimator, four case studies were developed. First, thigh angles (inputs) were used to estimate hip moments (outputs). Second, thigh angles were used to estimate knee moments. Third, ankle moments were estimated using thigh angles, and in the fourth case study, ankle moments were estimated using shank angles. Three different databases involving 106 subjects at different walking speeds were used to evaluate estimation quality. The testing procedure involved both inter-subject and intra-subject evaluations. The best estimation performance was observed when ankle moments were estimated from shank angles. The weakest estimation performance was observed when knee moments were estimated using thigh angles at 0.5 m/s. For this case, the estimation quality was much better at 1.5 m/s. Average RMS errors were 0.13&#x2013; 0.15, 0.10&#x2013; 0.13, and 0.09&#x2013; 0.12 [Nm/kg] for hip, knee and ankle moments, respectively. Average mean absolute errors MAEs were 0.10&#x2013; 0.11, 0.07&#x2013; 0.10, and 0.06&#x2013; 0.08 [Nm/kg] for hip, knee and ankle moments, respectively. Average correlation coefficients were 0.90&#x2013; 0.98 and 0.98&#x2013; 0.99 for hip and ankle moment estimations. The value for knee was comparable only at high speed (0.96 for 1.5 m/s), while it was less accurate at slow speed (0.71 for 0.5 m/s). In general, for all of the joints, the estimation accuracy was comparable with that of other studies, although one source of input was employed (either shank or thigh angle).

Multi-Joint Leg Moment Estimation During Walking Using Thigh or Shank Angles