Phase-Plane Based Model-Free Estimation of Steady-State Metabolic Cost

Kantharaju, Prakyath; Kim, Myunghee

doi:10.1109/access.2022.3205629

Cited by 6 publications

(2 citation statements)

References 60 publications

(88 reference statements)

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“…All participants wore the respiratory measurement device (K5, Cosmed, Rome, Italy). The Phase-plane based model-free estimation method 71 was used to estimate the metabolic cost from the measured respiratory data. The details of metabolic cost measurement were described in Kantharaju et al 55 .…”

Section: Methodsmentioning

confidence: 99%

Muscle coordination and recruitment during squat assistance using a robotic ankle–foot exoskeleton

Jeong

Haghighat

Kantharaju

et al. 2023

Sci Rep

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Squatting is an intensive activity routinely performed in the workplace to lift and lower loads. The effort to perform a squat can decrease using an exoskeleton that considers individual worker’s differences and assists them with a customized solution, namely, personalized assistance. Designing such an exoskeleton could be improved by understanding how the user’s muscle activity changes when assistance is provided. This study investigated the change in the muscle recruitment and activation pattern when personalized assistance was provided. The personalized assistance was provided by an ankle–foot exoskeleton during squatting and we compared its effect with that of the no-device and unpowered exoskeleton conditions using previously collected data. We identified four main muscle recruitment strategies across ten participants. One of the strategies mainly used quadriceps muscles, and the activation level corresponding to the strategy was reduced under exoskeleton assistance compared to the no-device and unpowered conditions. These quadriceps dominant synergy and rectus femoris activations showed reasonable correlations (r = 0.65, 0.59) to the metabolic cost of squatting. These results indicate that the assistance helped reduce quadriceps activation, and thus, the metabolic cost of squatting. These outcomes suggest that the muscle recruitment and activation patterns could be used to design an exoskeleton and training methods.

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Section: Methodsmentioning

confidence: 99%

Muscle coordination and recruitment during squat assistance using a robotic ankle–foot exoskeleton

Jeong

Haghighat

Kantharaju

et al. 2023

Sci Rep

Self Cite

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show abstract

“…The corresponding energy expenditure (metabolic cost) of the subject was measured using the respiratory metabolic measurement device (K5, Cosmed, Rome, Italy). The phase-plane based data-driven method was utilized to estimate the metabolic cost from the measured respiratory signals ( Kantharaju and Kim, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Predicting the metabolic cost of exoskeleton-assisted squatting using foot pressure features and machine learning

2023

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Introduction: Recent studies found that wearable exoskeletons can reduce physical effort and fatigue during squatting. In particular, subject-specific assistance helped to significantly reduce physical effort, shown by reduced metabolic cost, using human-in-the-loop optimization of the exoskeleton parameters. However, measuring metabolic cost using respiratory data has limitations, such as long estimation times, presence of noise, and user discomfort. A recent study suggests that foot contact forces can address those challenges and be used as an alternative metric to the metabolic cost to personalize wearable robot assistance during walking.Methods: In this study, we propose that foot center of pressure (CoP) features can be used to estimate the metabolic cost of squatting using a machine learning method. Five subjects’ foot pressure and metabolic cost data were collected as they performed squats with an ankle exoskeleton at different assistance conditions in our prior study. In this study, we extracted statistical features from the CoP squat trajectories and fed them as input to a random forest model, with the metabolic cost as the output.Results: The model predicted the metabolic cost with a mean error of 0.55 W/kg on unseen test data, with a high correlation (r = 0.89, p < 0.01) between the true and predicted cost. The features of the CoP trajectory in the medial-lateral direction of the foot (xCoP), which relate to ankle eversion-inversion, were found to be important and highly correlated with metabolic cost.Conclusion: Our findings indicate that increased ankle eversion (outward roll of the ankle), which reflects a suboptimal squatting strategy, results in higher metabolic cost. Higher ankle eversion has been linked with the etiology of chronic lower limb injuries. Hence, a CoP-based cost function in human-in-the-loop optimization could offer several advantages, such as reduced estimation time, injury risk mitigation, and better user comfort.

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A compliance control strategy of lower‐limb exoskeletons for energy‐efficient stance‐squat transitions with a payload

Hsiao,

2023

Asian Journal of Control

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This paper proposes an admittance control algorithm for lower‐limb exoskeletons to assist able‐bodied wearers in performing energy‐efficient stance‐squat transitions while carrying a payload of up to 20 kg. A two‐degree‐of‐freedom (DOF) model dedicated to the stance‐squat transition is derived, which takes into account the wearer's torso inclination angle and the vertical displacement of the hip joint. The torso inclination angle is obtained from an inertial measurement unit (IMU) located at the backpack of the exoskeleton. In addition, the moment caused by the torso plus the payload with respect to the hip joint is estimated. Then a model‐based disturbance observer (DOB) is used to estimate the wearer's joint torque as an indication of the motion intention, and the exoskeleton complies with the intention by following the desired joint angular velocity generated from the estimated wearer's joint torque through an admittance function. Experiments with different admittance parameters are conducted, and the energy consumption of the wearer is evaluated in terms of the normalized energy consumption index (NECI) and the physiological cost index (PCI). Furthermore, experimental comparisons of energy consumption are made for a subject carrying a payload of up to 20 kg and performing repeated stance‐squat transitions with and without the exoskeleton. The results show that a 50% reduction of energy consumption (in terms of PCI) is achieved by the exoskeleton with the proposed admittance control strategy.

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Phase-Plane Based Model-Free Estimation of Steady-State Metabolic Cost

Cited by 6 publications

References 60 publications

Muscle coordination and recruitment during squat assistance using a robotic ankle–foot exoskeleton

Muscle coordination and recruitment during squat assistance using a robotic ankle–foot exoskeleton

Predicting the metabolic cost of exoskeleton-assisted squatting using foot pressure features and machine learning

A compliance control strategy of lower‐limb exoskeletons for energy‐efficient stance‐squat transitions with a payload

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