Wearable robotic exoskeletons have emerged as promising technologies for enhancing gait rehabilitation and providing mobility assistance in individuals with neurological and musculoskeletal disorders. This narrative review summarizes recent advances in wearable robots—including both rigid exoskeletons and soft exosuits—and evaluates their clinical application across diverse conditions such as stroke, spinal cord injury, cerebral palsy, and Parkinson’s disease. For rehabilitation purposes, these devices enable repetitive, task-specific gait training that promotes motor learning, reduces therapist burden, and facilitates improvements in walking speed, balance, and endurance. Rigid exoskeletons provide substantial joint support and are particularly effective for patients with severe gait impairments, whereas soft exosuits offer lightweight assistance suited to individuals with milder deficits or fatigue, albeit with limited capacity to deliver high-torque support. Beyond rehabilitation, wearable robots are increasingly used as assistive devices to compensate for permanent gait limitations and restore mobility in daily life. However, widespread clinical adoption remains constrained by several challenges, including a lack of standardized protocols; limited evidence from large-scale, multicenter studies; and practical issues such as device weight, comfort, and ease of use in community settings. Recent developments—such as adaptive control algorithms, volition-adaptive assistance, and artificial intelligence integration—are addressing these barriers by enabling more personalized and responsive support. With continued research investment, user-centered design, and supportive policies, wearable exoskeletons hold considerable potential to improve independence, participation, and quality of life for individuals across a broad spectrum of mobility impairments.

Yoon SY, et al. Yonsei Med J. 2025 Jun;66:e57. https://doi.org/10.3349/ymj.2024.0399

Background: Preoperative gait analysis plays a crucial role in determining the necessity and correction angle for femoral derotational osteotomy (FDO). However, conventional musculoskeletal models used in gait analysis often fail to reflect patient-specific musculoskeletal characteristics, such as femoral and tibial deformities. This study evaluates the impact of a personalized musculoskeletal model incorporating these deformities on gait analysis and investigates the surgical outcomes of FDO based on kinematic changes derived from this personalized musculoskeletal model. Methods: A retrospective analysis was conducted on 254 limbs from 127 children with cerebral palsy (CP) who presented with increased femoral anteversion and underwent pre- and postoperative gait analyses. Kinematic data were generated using general and personalized musculoskeletal models developed in OpenSim. Patients were classified according to FDO status and the presence of excessive hip internal rotation (IR). Surgical outcomes were assessed based on postoperative changes in hip rotation. Subgroup analyses were performed to evaluate the model’s impact on surgical outcomes. Results: Of the 254 limbs, 92 underwent FDO. Patients with increased hip IR in the general model (Group A) had a higher good responder rate (88.2%) than those without (Group B, 17.2%). All limbs in Groups A1 and B1 (increased hip IR using personalized musculoskeletal models) had 100% favorable outcomes, whereas Groups A2 and B2 (not increased hip IR using personalized musculoskeletal models) showed favorable outcomes in 20% and 13.5%, respectively. Increased hip IR was more frequent in patients with external tibial rotation (p < 0.05). Surgical outcomes differed significantly between patients with and without increased hip IR in the personalized musculoskeletal model (χ² = 4.90, p = 0.027). Conclusion: Gait analysis using personalized musculoskeletal models improved surgical decision-making for FDO, leading to better outcomes in children with CP. Personalized musculoskeletal models better identified suitable FDO candidates and more accurately predict surgical outcomes than general models. Trial registration: Retrospectively registered.

Cha JM, et al. Brain Neurorehabil. 2019 Sep;13:e11. https://doi.org/10.12786/bn.2020.13.e11

Cha JM, et al. Yonsei Med J. 2026 Jan;67(1):56-61. https://doi.org/10.3349/ymj.2025.0003