Namho Kim

Soft wearable robots have gained widespread interest across various disciplines; however, they remain insufficient in overcoming the physical limitations of the human body. In particular, enhancing vertical jump height, a commonly used indicator of physical capability, requires improved actuator power density, stroke length, and soft structure efficiency. To address these challenges, the Jump‐Enhancing Textile Suit is proposed, which integrates the Pneumatic Energy‐Storing Propulsion Actuator (PESPA) and the Triarticular Kinetic‐Chained Structure (TKiCS) to assist jump performance. PESPA stores elastic energy under pneumatic pressure and releases it during the propulsive phase to augment human movement. TKiCS uses the kinetic chain mechanism to reduce anchoring points and fully harness the high stiffness region, thereby improving force transmission efficiency. Controlled vertical jump experiments with healthy adult participants are conducted. The suit increases jump height by 3.74 cm on average and up to 9.04 cm maximum, while also enhancing hip, knee, and ankle torques. Under isotonic testing, PESPA achieves a power density of 2298.69 W kg ⁻¹ and outperforms conventional pneumatic actuators. A dynamic model enables accurate force prediction and precise timing for effective assistance. These findings establish a practical foundation for pneumatic wearable robotics and suggest applications in jump augmentation, rehabilitation, and athletic performance.

Human-friendly robot designs are often inspired by human joints that exhibit lightweight, dexterity, and large compressive load capacity. However, there is a significant problem when attaching sensors to contact joints inspired by human joints. Attaching traditional sensors for obtaining information on a joint is substantially complicated by the skewed rotation axes. To solve this problem, we propose a novel contact joint sensor suitable for 3D curved surfaces. The proposed contact joint sensor is composed of a contact resistance force-sensor module for obtaining distributed pressure measurements utilized to estimate the joint information via a learning method. Each force-sensor array arranged in the desired shape on a 3D curved surface measures the surface pressure transmitted through a heterogeneous force-transmit layer. The learning-based model estimates the joint angle and torque values while maintaining the estimation performance even under varying load conditions. We validated the proposed contact joint sensor with experiments involving various load conditions. The average root-mean-squared error (RMSE) values of the flexion/extension and radial/ulnar rotation angles are $2.2\,^{\circ }$ and $1.7\,^{\circ }$ , respectively. In addition, estimations of the torque and tension at the contact joint show good agreement with the reference values despite changes in the load conditions.

Purpose: We propose a prediction model based on the damage features for fatigue crack growth in lap joints. Methods: Two features were extracted, and a crack quantification model was established. Each feature required to calculate the crack length was transformed to a parameter for a cycle. The model parameters were derived using Bayesian approaches through Markov chain Monte Carlo sampling, and the crack growth was predicted. The model accuracy was examined by comparing the obtained results with those derived using the Paris model. Results: Comparison of the crack growth determined using the feature-based model and Paris model as well as the measurement data indicated that the maximum and minimum error rates for the feature-based model were 0.67% and 0.08%, respectively. Conclusion: A model was developed to predict the crack length, which grows based on cycles. The feature-based model uses a small amount of data and can eliminate the uncertainty caused by the variability in the manufacturing of the materials.

The prosthetic arms generally face several challenges: most critically, they are not lightweight to the users and do not provide full degrees of freedom (DoF) for the arms. In order to address this problem, we propose a novel bio-inspired lightweight and high-DoF wrist for prosthetic arms. The proposed robotic wrist is lightweight, albeit more dexterous by simplifying the human wrist structure into a two-row ellipsoid joint and implementing novel ligament routing. The two-row ellipsoid structure effectively reduces the weight of the full-DoF wrist within sufficient ranges of motion (RoM) required for its mobility. A novel routing-by-ligament tunnel allows for the employment of stiffer ligaments in a compact structure, thereby securing the full DoF of the wrist without compromising its load capacity. We conducted a musculoskeletal simulation to show that the bioinspired wrist helps to increase the energy efficiency of the entire upper limb. Our experimental results demonstrated that the proposed wrist greatly reduces the weight of the entire prosthetic arm while improving its range of motion, tensile stiffness, and control performance.