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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. 

The foot center of pressure (COP) variability is an important indicator of balance, particularly relevant for rehabilitation and training using wearable lower limb exoskeletons. This study aimed to evaluate the effectiveness of our exoskeleton in assisting squatting motion using the COP variability as a metric. Six human subjects performed alternate squatting and standing movements while their foot pressure and COP trajectories were recorded using insole pressure sensors. The exercises were performed under three conditions: i) no device, ii) unpowered device, and iii) device with optimal stiffness. Results showed that the variability of the COP trajectory in the anterior-posterior direction of the foot during squatting tended to be lower for the optimal stiffness condition than the no device and unpowered device conditions, indicating the potential usefulness of the device in improving balance during squatting. This study has implications for human-inthe-loop optimization and balance control of the exoskeleton based on COP.