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"Assistive device"

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"Assistive device"

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;[Epub]     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
  • 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
  • 5,855 View
  • 215 Download
  • 5 Web of Science
  • 5 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
  • 4,717 View
  • 37 Download
  • 8 Web of Science
  • 7 Crossref
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