Objectively quantifying walking ability in degenerative spinal disorder patients using sensor equipped smart shoes
Open accessAbstract
Lumbar spinal stenosis (LSS) is a condition associated with the degeneration of spinal disks in the lower back. A significant majority of the elderly population experiences LSS, and the number is expected to grow. The primary objective of medical treatment for LSS patients has focused on improving functional outcomes (e.g., walking ability) and thus, an accurate, objective, and inexpensive method to evaluate patients' functional levels is in great need. This paper aims to quantify the functional level of LSS patients by analyzing their clinical information and their walking ability from a 10 m self-paced walking test using a pair of sensorized shoes. Machine learning algorithms were used to estimate the Oswestry Disability Index, a clinically well-established functional outcome, from a total of 29 LSS patients. The estimated ODI scores showed a significant correlation to the reported ODI scores with a Pearson correlation coefficient (r) of 0.81 and p<3.5×10(-11). It was further shown that the data extracted from the sensorized shoes contribute most to the reported estimation results, and that the contribution of the clinical information was minimal. This study enables new research and clinical opportunities for monitoring the functional level of LSS patients in hospital and ambulatory settings.
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Intraocular pressure can be monitored through measuring the forces acting on the contact lenses to manage glaucoma.
“Intraocular pressure can be monitored though measuring the forces acting on the contact lenses to manage glaucoma.”
From Wearables in Medicine (2018) · cites this paper as [49]
The Materials > Sensory Platform passage describes pressure sensors in shoe insoles, and the Conclusion discusses monitoring walking ability in patients with lumbar spinal stenosis. Searches for intraocular pressure, contact lenses, glaucoma, and forces acting on contact lenses found no passage addressing the claim. Page numbers were not available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredOcular diagnostic devices have undergone rapid development over the last decade.
“Ocular diagnostic devices have undergone rapid development over the last decade.49”
From Wearables in Medicine (2018) · cites this paper as [49]
The Discussion and Conclusion describe sensor-equipped shoes for assessing walking ability in patients with lumbar spinal stenosis; no page numbers are available in our copy. Searches for ocular diagnostic devices, ophthalmic devices, and development over the last decade found no passage addressing the claim. (LLM, conf=0.78)
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Yetisen, A. K., Martinez‐Hurtado, J. L., Ünal, B., Khademhosseini, A., & Butt, H. (2018) · Advanced Materials · doi:10.1002/adma.201706910 · Cited by 569 · see citing works
- Authors
- Lee, S. I., Park, E., Huang, A., Mortazavi, B., Hayward Garst, J., Jahanforouz, N., Espinal, M., Siero, T., Pollack, S., Afridi, M., Daneshvar, M., Ghias, S., C. Lu, D., & Sarrafzadeh, M.
- Affiliations
- Harvard University (US)
- University of California, Los Angeles (US)
- Corresponding author
- Lee, Sunghoon Ivan
- Venue
- Medical Engineering & Physics
- Publisher
- IOP Publishing
- Published
- 2016-03-09
- Volume
- 38
- Issue
- 5
- Pages
- 442-449
- ISSN
- 1350-4533 (ISSN-L)
- 1873-4030
- PMC
- PMC6470359
- OpenAlex
- W2293431684
- Cited by
- 41
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 7 · 2022: 8 · 2021: 4 · 2020: 5 · 2019: 3 · 2018: 2 · 2017: 4 · 2016: 1
- FWCI
- 3.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Medical Engineering & Physics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Funders
- NCATS
- UCLA (IRB# 12–000009)
- CTSI (UL1TR000124)
- Topics
- Health Sciences › Medicine › Pathology and Forensic Medicine › Spine and Intervertebral Disc Pathology
- Health Sciences › Medicine › Pharmacology › Musculoskeletal pain and rehabilitation
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- lumbar spinal stenosis, walking ability, wearable sensors, Oswestry Disability Index, functional outcome assessment, machine learning prediction, elderly patients
- MeSH terms
- Machine Learning, Adult, Aged, Female, Gait, Humans, Lumbar Vertebrae, Male, Middle Aged, Monitoring, Physiologic, Postoperative Period, Pressure, Shoes, Spinal Stenosis, Walking, Preoperative Period, Spatio-Temporal Analysis
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