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• W2247265570 abstract "Effectiveness of hap tic guidance systems depends on how humans adapt their neuromuscular response to the force feedback. A quantitative insight into adaptation of neuromuscular response can be obtained by identifying neuromuscular dynamics. Since humans are likely to vary their neuromuscular response during realistic control scenarios, there is a need for methods that can identify time-varying neuromuscular dynamics. In this work an identification method is developed which estimates the impulse response of time-varying neuromuscular system by using a Recursive Least Squares (RLS) method. The proposed method extends the commonly used RLS-based method by employing the pseudo inverse operator instead of the inverse operator. This results in improved robustness to external noise. The method was validated in a human in-the-loop experiment. The neuromuscular estimates given by the proposed method were more accurate than those obtained with the commonly used RLS-based method." @default.
• W2247265570 created "2016-06-24" @default.
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• W2247265570 date "2015-10-01" @default.
• W2247265570 modified "2023-09-23" @default.
• W2247265570 title "Identifying Time-Varying Neuromuscular Response: A Recursive Least-Squares Algorithm with Pseudoinverse" @default.
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• W2247265570 doi "https://doi.org/10.1109/smc.2015.535" @default.
• W2247265570 hasPublicationYear "2015" @default.
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