Human-in-the-loop (HITL) optimization can personalize exosuit assistance, but respirometry-based cost functions require bulky equipment, and proxy-based alternatives often require additional sensors or leave their relationship with metabolism unquantified. Our previous onboard-sensor approach optimized a single objective based on positive mechanical power (PMP). This exploratory study investigated whether stride time (ST), a gait response measured by the exosuit’s thigh-mounted inertial sensors, better represents metabolic reduction. Using grid-search data from three participants walking at 1 m/s on a 5° incline with a hip extension exosuit (six subject-day cases), we evaluated PMP, ST, and their combination by multiple linear regression, termed the multi-variable cost function (MVCF), with leave-one-subject-out cross-validation. ST predicted metabolic reduction numerically better than PMP (r = 0.63 ± 0.14 vs. 0.51 ± 0.37), and the MVCF performed best (r = 0.68 ± 0.11; lowest mean absolute error), although no difference was statistically significant. The MVCF, computed solely from onboard sensors, served as the objective of Bayesian HITL optimization in five participants. The optimized assistance significantly reduced net metabolic rate by 13.3%, 7.5%, and 5.2% versus power-off, no-suit, and biomimetic conditions, respectively. Combining device-side mechanical power with human-side gait timing may provide an onboard-sensor cost function grounded in measured metabolic data.

Active exosuits provide various assistive force profiles but are limited by battery life, weight, and complex maintenance requirements. Passive exosuits, by contrast, are economical and lightweight while also offering unlimited usage times; however, due to their fixed stiffness levels, they can provide only a limited set of optimized assistive force profiles for different movements. To address these issues, this paper proposes a new variable elastic band for semi-passive exosuits. It comprises rubber bands and webbings connected in parallel, with the elongation of the rubber bands restricted according to the webbing length. By connecting these segments in series, a range of elasticities can be generated. Experimental results confirmed that the band could generate different stiffness levels, which were accurately predicted with an average coefficient of determination (R²) of 0.9985 and an average root mean square error of 0.8993. Additionally, based on tests involving participants wearing the device, the variable elastic band effectively modulated the assistive force profile. These findings overcome the previous limitations of passive components, opening the door to future research on enhancing the efficiency of passive systems and enabling further customization.

Wearable robots can improve human walking economy; however, their effectiveness depends on user adaptation to assistance. This study introduces a framework for real-time estimation of user adaptation that relies only on wearable sensor data during operation. Metabolic measurements were used solely to establish the ground truth adaptation curves for model training and validation but are not required for real-time inference. Five healthy adults completed six days of treadmill walking while wearing a soft hip exosuit that provided hip extension assistance. Thigh-mounted inertial measurement units recorded step timing and hip-angle trajectories, from which three variability-based features (step-frequency variability, maximum hip-flexion variability, and maximum hip-extension variability) were extracted. A Long Short-Term Memory (LSTM) model used these gait-variability inputs to estimate each user’s adaptation level relative to a metabolic cost benchmark obtained from respiratory gas analysis. Across sessions, the metabolic cost decreased by 9.0 ± 5.6% from Day 1 to Day 6 (p < 0.01) with a mean time constant of 202 ± 78 min, In contrast, the variability in step frequency, maximum hip flexion, and maximum hip extension decreased by 66.4 ± 6.8%, 37.9 ± 24.2%, and 42.8 ± 10.6%, respectively, indicating that these reductions were users’ progressive adaptation to the exosuit’s assistance. Under leave-one-subject-out (LOSO) evaluation across five participants, 59.2% of the model predictions fell within ±10 percentage points of the metabolic cost–based adaptation curve. These results suggest that simple kinematic variability measured with wearable sensors can track user adaptation and support practical approaches to real-time monitoring. Such capability can facilitate adaptive control and training protocols that personalize exosuit assistance.