Exoskeletons can assist humans during squatting and the assistance has the potential to reduce the physical demands. Although several squat assistance methods are available, the effect of personalized assistance on physical effort has not been examined. We hypothesize that personalized assistance will reduce the physical effort of squatting. We developed a human-in-the-loop Bayesian optimization scheme to minimize the metabolic cost of squatting using a unilateral ankle exoskeleton. The optimization identified subject-specific assistance parameters for ascending and descending during squatting and took 15.8 min on average to converge. The subject-specific optimized condition reduced metabolic cost by 19.9% and rectus femoris muscle activity by 28.7% compared to the condition without the exoskeleton with a higher probability of improvement compared to a generic condition. In an additional study with two participants, the personalized condition presented higher metabolic cost reduction than the generic condition. These reductions illustrate the importance of personalized ankle assistance using an exoskeleton for squatting, a physically intensive activity, and suggest that such a method can be applied to minimize the physical effort of squatting. Future work can investigate the effect of personalized squat assistance on fatigue and the potential risk of injury. Index Terms-Squatting, human-in-the-loop, ankle-foot orthosis, and exoskeleton. I. INTRODUCTIONS QUATTING is a physically intensive and injury-prone task. Joint loading during squatting is higher than it is in other daily activities, such as walking [1], [2] and standing. Joint loads in squatting have increased contact stress and injuries in the tibiofemoral and patellofemoral joints [3]-[5].

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.

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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.