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Wearable Robots for Rehabilitation and Assistance of Gait: A Narrative Review
Jun Min Cha, Juntaek Hong, Jehyun Yoo, Dong-wook Rha
Ann Rehabil Med 2025;49(4):187-195.   Published online August 18, 2025
DOI: https://doi.org/10.5535/arm.250093
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.

Citations

Citations to this article as recorded by  
  • The potential of robotics: A systematic review of neuroplastic changes following advanced lower limb rehabilitation in neurological disorders
    Rocco Salvatore Calabrò, Andrea Calderone, Laura Simoncini, Antonino Naro, Lorenzo Octavio Small Haughton, Angelo Quartarone, Carl Froilan D. Leochico
    Neuroscience & Biobehavioral Reviews.2026; 180: 106459.     CrossRef
  • A review of treatment methods for movement disorders
    Mahdi Khezri, Shakiba Afsar
    Behavioural Brain Research.2026; 500: 115979.     CrossRef
  • Influence of Rehabilitation Aid Use on Obstacle Height During Gait in Patients with Foot Drop: A Case Series Study
    Joonsung Park, Himchan Shim, Changho Jang, Hanyang Yin, Jongbin Kim
    Healthcare.2025; 13(22): 2984.     CrossRef
  • A Wearable System for Knee Osteoarthritis: Based on Multimodal Physiological Signal Assessment and Intelligent Rehabilitation
    Jingyi Hu, Shuyi Wang, Yichun Shen, Xinrong Miao
    Sensors.2025; 25(23): 7334.     CrossRef
  • Ethical Horizons in Robotic Rehabilitation: Ensuring Safe AI Use Under the EU AI Act
    Rocco Salvatore Calabrò
    Medical Sciences.2025; 13(4): 317.     CrossRef
  • 5,989 View
  • 172 Download
  • 3 Web of Science
  • 5 Crossref
Original Articles
Determining the Most Appropriate Assistive Walking Device Using the Inertial Measurement Unit-Based Gait Analysis System in Disabled Patients
Junhee Lee, Chang Hoon Bae, Aeri Jang, Seoyon Yang, Hasuk Bae
Ann Rehabil Med 2020;44(1):48-57.   Published online February 29, 2020
DOI: https://doi.org/10.5535/arm.2020.44.1.48
Objective
To evaluate the gait pattern of patients with gait disturbances without consideration of defilades due to assistive devices. This study focuses on gait analysis using the inertial measurement unit (IMU) system, which can also be used to determine the most appropriate assistive device for patients with gait disturbances.
Methods
Records of 18 disabled patients who visited the Department of Rehabilitation from May 2018 to June 2018 were selected. Patients’ gait patterns were analyzed using the IMU system with different assistive devices to determine the most appropriate device depending on the patient’s condition. Evaluation was performed using two or more devices, and the appropriate device was selected by comparing the 14 parameters of gait evaluation. The device showing measurements nearer or the nearest to the normative value was selected for rehabilitation.
Results
The result of the gait evaluation in all 18 patients was analyzed using the IMU system. According to the records, the patients were evaluated using various assistive devices without consideration of defilades. Moreover, this gait analysis was effective in determining the most appropriate device for each patient. Increased gait cycle time and swing phase and decreased stance phase were observed in devices requiring significant assistance.
Conclusion
The IMU-based gait analysis system is beneficial in evaluating gait in clinical fields. Specifically, it is useful in evaluating patients with gait disturbances who require assistive devices. Furthermore, it allows the establishment of an evidence-based decision for the most appropriate assistive walking devices for patients with gait disturbances.

Citations

Citations to this article as recorded by  
  • Gait detection of lower limb exoskeleton robot integrating visual perception and geometric features
    BinHao Huang, Jian Lv, Ligang Qiang
    Intelligent Service Robotics.2025; 18(3): 529.     CrossRef
  • Gait phase recognition method for lower limb exoskeleton robot based on SE channel attention mechanism enhanced TCN-SVM
    BinHao Huang, Jian Lv, Ligang Qiang
    Computer Methods in Biomechanics and Biomedical Engineering.2025; : 1.     CrossRef
  • Designing a Gait Recognition Algorithm for Older Adults Using Mobility Aids: Prospective Cohort Study
    Samantha Jeane Ray, Jung In Koh, Amanda Mae Liberty, Tracy Anne Hammond, Paula Kay Shireman
    JMIR Formative Research.2025; 9: e68669.     CrossRef
  • GMM‐LIME explainable machine learning model for interpreting sensor‐based human gait
    Mercy Mawia Mulwa, Ronald Waweru Mwangi, Agnes Mindila
    Engineering Reports.2024;[Epub]     CrossRef
  • Modelling and analysis of orthoses generated whole-body vertical vibrations impact on limb stability and compliant dynamics in a ramp gait
    Imran Mahmood, Muhammad Zia Ur Rahman, Abbas A. Dehghani-Sanij
    Biomedical Signal Processing and Control.2023; 79: 104163.     CrossRef
  • Depth-aware pose estimation using deep learning for exoskeleton gait analysis
    Yachun Wang, Zhongcai Pei, Chen Wang, Zhiyong Tang
    Scientific Reports.2023;[Epub]     CrossRef
  • 7,487 View
  • 219 Download
  • 6 Web of Science
  • 6 Crossref
Comparison of the Using Ability Between a Smartphone and a Conventional Mobile Phone in People With Cervical Cord Injury
Seongkyu Kim, Bum-Suk Lee, Ji Min Kim
Ann Rehabil Med 2014;38(2):183-188.   Published online April 29, 2014
DOI: https://doi.org/10.5535/arm.2014.38.2.183
Objective

To investigate the ability of spinal cord injury (SCI) patients in the use mobile cellular devices, especially the smartphone.

Methods

Seventeen people with motor complete cervical SCI participated in the study. The assist-devices deemed most fitting were introduced to the patients: a mouth stick, multifunctional splint, activities of daily living (ADL) splint, universal cuff or none of the above. To determine the effective devices, a Multi-Directional Click Test (MDCT), Phone Number Test (PNT), and individual satisfaction inquiry were used. The most appropriate assist device was selected by MDCT. Subsequently PNT and individual satisfaction inquiry were performed with the conventional model and compared.

Results

Those with C4 cord injury chose mouth stick. Those with C5 cord injury chose multifunctional splint (3 people) and ADL splint (2 people). Those with C6 cord injury chose universal cuff (3 people) or bare hands only. Those with C7 cord injury chose universal cuff (3 people). With a smartphone, all participants were able to complete the PNT. With a conventional model, only twelve participants (71%) were able to complete the same test. While it took 26.8±6.8 seconds with a conventional model to complete PNT, the same test took 18.8±10.9 seconds to complete with a smartphone (p<0.05). Overall, participants expressed higher satisfaction when using a smartphone.

Conclusion

The results offer a practical insight into the appropriate assist devices for SCI patients who wish to use mobile cellular devices, particularly smartphones. When the SCI patients are given the use of a smartphone with the appropriate assist devices, the SCI patients are expected to access mobile cellular device faster and with more satisfaction.

Citations

Citations to this article as recorded by  
  • Smartphone accessibility: understanding the lived experience of users with cervical spinal cord injuries
    Richard Armstrong-Wood, Chrysovalanto Messiou, Amber Kite, Elisabeth Joyce, Stephanie Panousis, Hannah Campbell, Arnaud Lauriau, Julia Manning, Tom Carlson
    Disability and Rehabilitation: Assistive Technology.2024; 19(4): 1434.     CrossRef
  • Internet of things (IoT)-based assistive system for patients with spinal muscular atrophy (SMA): a case report
    José Varela-Aldás, William Avila-Armijos, Guillermo Palacios-Navarro
    Disability and Rehabilitation: Assistive Technology.2024; 19(7): 2498.     CrossRef
  • Barriers and Facilitators to eHealth Technology Use Among Community-Dwelling Individuals With Spinal Cord Injury: A Qualitative Study
    Gurkaran Singh, Laura Nimmon, Bonita Sawatzky, W. Ben Mortenson
    Topics in Spinal Cord Injury Rehabilitation.2022; 28(2): 196.     CrossRef
  • Patients’ Perspectives on the Usability of a Mobile App for Self-Management following Spinal Cord Injury
    Gurkaran Singh, Megan MacGillivray, Patricia Mills, Jared Adams, Bonita Sawatzky, W. Ben Mortenson
    Journal of Medical Systems.2020;[Epub]     CrossRef
  • Effects of the Computer Desk Level on the Musculoskeletal Discomfort of Neck and Upper Extremities and EMG Activities in Patients with Spinal Cord Injuries
    Bo-Ra Kang, Jin-Gang Her, Ju-Sang Lee, Tae-Sung Ko, Young-Youl You
    Occupational Therapy International.2019; 2019: 1.     CrossRef
  • Towards an Affordable Assistive Device for Personal Autonomy Recovery in Tasks Required of Manual Dexterity
    Edwin Daniel Ona Simbana, Gabriel Barroso de Maria, Carlos Balaguer, Alberto Jardon Huete
    IEEE Access.2018; 6: 26338.     CrossRef
  • Disability and haptic mobile media
    Gerard Goggin
    New Media & Society.2017; 19(10): 1563.     CrossRef
  • 5,732 View
  • 37 Download
  • 8 Web of Science
  • 7 Crossref
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