Automated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community setting
Open accessAbstract
BACKGROUND: Falls are a leading cause of morbidity and mortality among older adults and patients with neurological disease like Parkinson's disease (PD). Self-report of missteps, also referred to as near falls, has been related to fall risk in patients with PD. We developed an objective tool for detecting missteps under real-world, daily life conditions to enhance the evaluation of fall risk and applied this new method to 3 day continuous recordings. METHODS: 40 patients with PD (mean age ± SD: 62.2 ± 10.0 yrs, disease duration: 5.3 ± 3.5 yrs) wore a small device that contained accelerometers and gyroscopes on the lower back while participating in a protocol designed to provoke missteps in the laboratory. Afterwards, the subjects wore the sensor for 3 days as they carried out their routine activities of daily living. An algorithm designed to automatically identify missteps was developed based on the laboratory data and was validated on the 3 days recordings. RESULTS: In the laboratory, we recorded 29 missteps and more than 60 hours of data. When applied to this dataset, the algorithm achieved a 93.1% hit ratio and 98.6% specificity. When we applied this algorithm to the 3 days recordings, patients who reported two falls or more in the 6 months prior to the study (i.e., fallers) were significantly more likely to have a detected misstep during the 3 day recordings (p = 0.010) compared to the non-fallers. CONCLUSIONS: These findings suggest that this novel approach can be applied to detect missteps during daily life among patients with PD and will likely help in the longitudinal assessment of disease progression and fall risk.
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Quantification of missteps and risk of falling was shown to discriminate non-fallers and fallers.
“Quantification of missteps and risk of falling was shown to discriminate non-fallers and fallers [35, 36].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [36]
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“[127, 134–136, 139–141] One activity-monitoring device (DynaPort Hybrid) was able to differentiate between ON/ OFF phases and detect “missteps/ near falls” in people with PD in the clinic and home environments.[136] Participants wore the device in the clinic while missteps were induced, an algorithm was developed to d…”
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- Authors
- Iluz, T., Gazit, E., Herman, T., Sprecher, E., Brozgol, M., Giladi, N., Mirelman, A., & Hausdorff, J. M.
- Affiliations
- Tel Aviv Sourasky Medical Center (IL)
- Tel Aviv University (IL)
- Corresponding author
- Hausdorff, Jeffrey M
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2014-04-03
- Volume
- 11
- Issue
- 1
- Pages
- 48-48
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC3978002
- OpenAlex
- W2171846896
- Cited by
- 76
- References
- 32
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 9 · 2021: 9 · 2020: 10 · 2019: 8 · 2018: 5 · 2017: 5 · 2016: 2 · 2015: 4
- FWCI
- 9.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- European Commission (FP7 ICT, ICT-2011, FP7‐ICT‐2011, 288878, FP7-ICT-2011-7)
- Israel Science Foundation
- National Institute on Aging (R21AG034227)
- FP7 Information and Communication Technologies
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Keywords
- Parkinson's disease, near-falls, fall risk, accelerometer and gyroscope, wearable sensors, community ambulation, activities of daily living, fall risk assessment, older adults
- MeSH terms
- Accidental Falls, Aged, Algorithms, Automation, Female, Gait, Humans, Male, Middle Aged, Monitoring, Physiologic, Parkinson Disease, Walking, Accelerometry
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