Visual guidance can help with the use of a robotic exoskeleton during human walking

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.

Section: Discussionmentioning

confidence: 99%

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

Jeong

Haghighat

et al. 2023

Sci Rep

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“…As a result, participants appear to maintain consistent squat posture, shown by similar estimated ankle angles. Future studies could include visual feedback with instruction on the subject's squat posture to further help consistent squat posture [65]. In addition, a motion capture system could be used to more objectively calculate kinematics to understand the underlying mechanism.…”

Section: Discussionmentioning

confidence: 99%

“…We trained the value and target network using the previously collected data [65] and data from Day1 of the squat study. In the training data, we generated a categorical label of stop and not-stop conditions by manually calculating the steady-state for each condition.…”

Section: Metabolic Estimation With Early Stopping Processmentioning

confidence: 99%

Reducing Squat Physical Effort Using Personalized Assistance From an Ankle Exoskeleton

IEEE Trans. Neural Syst. Rehabil. Eng.

Jeong

Ramadurai

et al. 2022

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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 maximum steady-state prediction thresholds were 900 W for walking and squatting conditions and 1800 W for running. The selected threshold was two-times the maximum metabolic rate observed in previous applications [13], [49]. This method was successfully used in real-time HIL exoskeleton parameter optimization to minimize the metabolic cost of squatting [6].…”

Section: B Metabolic Rate Regression In the Phase-plane Estimationmentioning

confidence: 99%

“…We used the data (N = 10) previously collected from a walking study using a unilateral ankle exoskeleton (Humotech, USA) [49]. This study included the seven walking conditions on day one and 13 conditions on day two.…”

Section: ) Walking Able-bodiedmentioning

confidence: 99%

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

Kim

2022

IEEE Access

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The steady-state metabolic cost has been used to assess the performance of wearable robotic devices. Recently, real-time assessment of this metabolic cost has been employed in an objective function when optimizing robotic parameters for an individual user, thus personalizing the assistance to minimize human effort. However, the long estimation time needed for the model-based approach to metabolic cost estimation limits the optimization only to light-intensity activities. Here, we hypothesized that model-free, phase-plane estimation (PPE) would reduce the estimation time for the steady-state metabolic cost. First, we developed a phase-plane representation to classify the steady-state (where the change in metabolic rate is zero) and the transient metabolic dynamics. Second, we approximated the transient metabolic dynamics using a data-driven Gaussian mixture model and a real-time respiratory measure. We compared the performance of PPE with that of the model-based method by examining (1) walking performance assisted by a robotic prosthetic foot for individuals with and without Below Knee Amputation (BKA) and (2) squatting and running performance for individuals without BKA. PPE reduced the steady-state estimation time during walking for individuals with and without BKA by 31% and 40%, respectively. It also reduced estimation time by 56% and 24% for squatting and running conditions. These significant reductions in estimation time suggest that the data-driven PPE method can be used to rapidly estimate physical effort when personalizing the assistance from wearable robots. This expands the ability to conduct individual optimization for subjects engaged in physical intensive activities or for individuals with reduced physical strength.INDEX TERMS Rehabilitation robotics, Exoskeletons, Prosthetic limbs, Human-robot interaction, Human in the loop optimization.