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Electrocardiogram (EMG) signals play a significant role in decoding musclecontraction information for robotic hand prosthesis controllers. Widely applieddecoders require large amount of EMG signals sensors, resulting in complicatedcalculations and unsatisfactory predictions. By the biomechanical process ofsingle degree-of-freedom human hand movements, only several EMG signals areessential for accurate predictions. Recently, a novel predictor of handmovements adopts a multistage Sequential, Adaptive Functional Estimation (SAFE)method based on historical Functional Linear Model (FLM) to select importantEMG signals and provide precise projections.  However, SAFE repeatedly performs matrix-vector multiplications with a denserepresentation matrix of the integral operator for the FLM, which iscomputational expansive. Noting that with a properly chosen basis, therepresentation of the integral operator concentrates on a few bands of thebasis, the goal of this study is to develop a fast Multiscale SAFE (MSAFE)method aiming at reducing computational costs while preserving (or evenimproving) the accuracy of the original SAFE method. Specifically, a multiscalepiecewise polynomial basis is adopted to discretize the integral operator forthe FLM, resulting in an approximately sparse representation matrix, and thenthe matrix is truncated to a sparse one. This approach not only acceleratescomputations but also improves robustness against noises. When applied to realhand movement data, MSAFE saves 85%$\sim$90% computing time compared with SAFE,while producing better sensor selection and comparable accuracy. In asimulation study, MSAFE shows stronger stability in sensor selection andprediction accuracy against correlated noise than SAFE.

Fast Multiscale Functional Estimation in Optimal EMG Placement for Robotic Prosthesis Controllers