Physiological Indicators of Fluency and Engagement during Sequential and Simultaneous Modes of Human-Robot Collaboration.

Occupational ApplicationsAn understanding of fluency in human-robot teaming from a physiological standpoint is still incomplete. In our experimental study involving 24 participants, we designed a scenario for shared-space human-robot collaboration (HRC) for a material sorting task. When compared to a sequential mode of interaction, the simultaneous mode resulted in significantly higher perceptions of fluency and engagement, primarily by reducing human idle time. These observations were complemented by significant changes in physiological responses, such as ECG entropy and low frequency power. These responses could predict fluency and engagement with accuracies of 90 and 97%, respectively. Notably, the perception of fluency and preferred mode of interaction were influenced by individual preferences. Hence, it is crucial to consider both physiological responses and user preferences when designing HRC systems, to ensure a positive experience with the robot teammate and to foster engagement in long-term teamwork. Furthermore, these signals can be obtained using a single robust, low-cost, and comfortable sensor.

Background In the current industry, a key enabler of flexible manufacturing is human-robot collaboration (HRC), a scenario wherein a human and a robot interact and work together in a shared space to accomplish a common task. In HRC, the timing and coordination between the human and robot are crucial factors that impact the fluency, efficacy, and acceptance of human-robot teams.Purpose Experimental research on the physiological indicators of fluency in human-robot collaborative tasks in a shared workspace is still in its infancy. We posit that by relating the mental perceptions of fluency to features from physiological signals, we could bring more clarity to the complex mapping between subjective and objective measures of fluency.Methods Twenty-four participants (12 males and 12 females), with mean (SD) age = 25.7 (2.9) years, completed an experimental study. We investigated the effects of interaction mode (sequential, simultaneous) and level of human involvement (low, medium, high) on perceived fluency, engagement, performance, and physiological response (heart rate variability = HRV) in a collaborative item sorting task.Results The simultaneous mode of interaction and a higher level of human involvement led to higher ratings for fluency and engagement, along with ECG changes, specifically an 11.6% increase in low-frequency power and a 3% reduction in information entropy. Using machine learning, these HRV features could predict perceived fluency and engagement with 90 and 97% accuracy, respectively.Conclusion Our results indicate that a human operator's perceived fluency in human-robot collaborative tasks can be measured using HRV metrics. Our findings expand the current fluency framework from a physiological perspective and offer additional objective measures derived from HRV, which could be practically applied to improve the design and optimization of HRC systems.

Effect of hip abduction assistance on metabolic cost and balance during human walking.

The use of wearable robots to provide walking assistance has rapidly grown over the past decade, with notable advances made in robot design and control methods toward reducing physical effort while performing an activity. The reduction in walking effort has mainly been achieved by assisting forward progression in the sagittal plane. Human gait, however, is a complex movement that combines motions in three planes, not only the sagittal but also the transverse and frontal planes. In the frontal plane, the hip joint plays a key role in gait, including balance. However, wearable robots targeting this motion have rarely been investigated. In this study, we developed a hip abduction assistance wearable robot by formulating the hypothesis that assistance that mimics the biological hip abduction moment or power could reduce the metabolic cost of walking and affect the dynamic balance. We found that hip abduction assistance with a biological moment second peak mimic profile reduced the metabolic cost of walking by 11.6% compared with the normal walking condition. The assistance also influenced balance-related parameters, including the margin of stability. Hip abduction assistance influenced the center-of-mass movement in the mediolateral direction. When the robot assistance was applied as the center of mass moved toward the opposite leg, the assistance replaced some of the efforts that would have otherwise been provided by the human. This indicates that hip abduction assistance can reduce physical effort during human walking while influencing balance.

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

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.

Reducing Squat Physical Effort Using Personalized Assistance From an Ankle Exoskeleton.

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.

Comparison Between 5- and 1-Year Outcomes Using Cutoff Values of Pressure Drop Coefficient and Fractional Flow Reserve for Diagnosing Coronary Artery Diseases.

BACKGROUND: The current pressure-based coronary diagnostic index, fractional flow reserve (FFR), has a limited efficacy in the presence of microvascular disease (MVD). To overcome the limitations of FFR, the objective is to assess the recently introduced pressure drop coefficient (CDP), a fundamental fluid dynamics-based combined pressure-flow index. METHODS: We hypothesize that CDP will result in improved clinical outcomes in comparison to FFR. To test the hypothesis, chi-square test was performed to compare the percent major adverse cardiac events (%MACE) at 5 years between (a) FFR < 0.75 and CDP > 27.9 and (b) FFR < 0.80 and CDP > 25.4 groups using a prospective cohort study. Furthermore, Kaplan-Meier survival curves were compared between the FFR and CDP groups. The results were considered statistically significant for p < 0.05. The outcomes of the CDP arm were presumptive as clinical decision was solely based on the FFR. RESULTS: For the complete patient group, the %MACE in the CDP > 27.9 group (10 out of 35, 29%) was lower in comparison to the FFR < 0.75 group (11 out of 20, 55%), and the difference was near significant (p = 0.05). The survival analysis showed a significantly higher survival rate (p = 0.01) in the CDP > 27.9 group (n = 35) when compared to the FFR < 0.75 group (n = 20). The results remained similar for the FFR = 0.80 cutoff. The comparison of the 5-year MACE outcomes with the 1-year outcomes for the complete patient group showed similar trends, with a higher statistical significance for a longer follow-up period of 5 years. CONCLUSION: Based on the MACE and survival analysis outcomes, CDP could possibly be an alternate diagnostic index for decision-making in the cardiac catheterization laboratory. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, identifier NCT01719016.