Abstract-In this work we presented a low-power magnetic measurement system based on only two Hall-effect elements and a permanent magnet integrated into a smart knee prosthesis to accurately measure knee flexion-extension. The smart prosthesis was affixed in a robotic knee simulator that provides the squat movements, and also replicates different patterns of recorded gait from subjects. The squat movements were used to build linear and locally linear neuro-fuzzy estimators to translate the magnetic measurements into knee flexion angle. The simulated gait patterns then used to validate the models. The locally linear neuro-fuzzy estimator showed a clear benefit comparing to the linear regression models, by sequentially splitting the measurement space into subregions and find local fuzzy models for each region. The obtained RMS errors on test data were lower than 1.3° for the neuro-fuzzy estimates representing smaller than 3% of range of rotation. The result was compared with the estimates from a previously-designed configuration of three 2-D anisotropic magnetoresistive (AMR) sensors which tested in mentioned setup. We showed that by using the neurofuzzy model for two Hall-effect elements, similar performance to the AMR-based estimator can be obtained while the power consumption can be reduced more than three folds.
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