Data-driven modeling of fatigue effects following repeated muscular contractions

Doshmanziari, Roya; Jackson, Roxanne R.; Varagnolo, Damiano; Knorn, Steffi

doi:10.1109/cdc51059.2022.9992949

Cited by 3 publications

(1 citation statement)

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“…The proposed compartmental model is an extension of the work in [15] and involves active muscles m a , fatigued muscles m f , and resting muscles m r , where the total number of muscular units is M . Note that the underlying true system is considered to be the more complex model derived in [16] where cramping muscles m c are additionally used to capture a particular dynamic often observed when people perform Kegel exercises, which is a more realistic situation where the underlying system is more complex than the proposed model. We consider here, however, the original model in [14], where the discrete-time, time-invariant, control-affine system is given by…”

Section: A Case Study -Designing Kegel Exercisesmentioning

confidence: 99%

A Dual Control Approach to Solve Exploration Versus Exploitation Trade-Offs in the Design of Personalized Physical Exercise Sessions

Jackson

Varagnolo

Knorn

2023

IEEE Control Syst. Lett.

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Using biofeedback in medical therapies has proven to be effective for adapting patient behaviors while keeping the patients engaged and motivated in an exercise session. This paper considers general problems in personalized exercise sessions where the input is opportune biofeedback and the session goal is to maximize a particular exercise effect. Due to the individual differences between patients and their physiological signals, however, personalized patient models also need to be identified. With the two objectives: 1) maximize a training effect with minimal control effort, and 2) identify the individualized patient model, we have a typical exploration vs. exploration trade-off. Control problems of this form are called dual control problems. In this paper, we formulate a dual control problem for a personalized exercise session and test the approach against classical optimal control and optimal experimental design approaches in an illustrative example of performing Kegel exercises where the control and identification goals conflict with each other.

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Section: A Case Study -Designing Kegel Exercisesmentioning

confidence: 99%