Identifying time-varying neuromuscular system with a recursive least-squares algorithm: a Monte-Carlo simulation study

A human-centered design of haptic aids aims at tuning the force feedback based on the effect it has on human behavior. For this goal, a better understanding of the influence of haptic aids on the pilot neuromuscular response becomes crucial. In realistic scenarios, the neuromuscular response can continuously vary depending on many factors, such as environmental factors or pilot fatigue. This paper presents a method that online estimates time-varying neuromuscular dynamics during force-related tasks. This method is based on a Recursive Least Squares (RLS) algorithm and assumes that the neuromuscular response can be approximated by a Finite Impulse Response filter. The reliability and the robustness of the method were investigated by performing a set of Monte-Carlo simulations with increasing level or remnant noise. Even with high level of remnant noise, the RLS algorithm provided accurate estimates when the neuromuscular dynamics were constant or changed slowly. With instantaneous changes, the RLS algorithm needed almost 8s to converge to a reliable estimate. These results seem to indicate that RLS algorithm is a valid tool for estimating online time-varying admittance.

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