Continuous Monitoring of Turning in Patients with Movement Disability
Open accessAbstract
Difficulty with turning is a major contributor to mobility disability and falls in people with movement disorders, such as Parkinson’s disease (PD). Turning often results in freezing and/or falling in patients with PD. However, asking a patient to execute a turn in the clinic often does not reveal their impairments. Continuous monitoring of turning with wearable sensors during spontaneous daily activities may help clinicians and patients determine who is at risk of falls and could benefit from preventative interventions. In this study, we show that continuous monitoring of natural turning with wearable sensors during daily activities inside and outside the home is feasible for people with PD and elderly people. We developed an algorithm to detect and characterize turns during gait, using wearable inertial sensors. First, we validate the turning algorithm in the laboratory against a Motion Analysis system and against a video analysis of 21 PD patients and 19 control (CT) subjects wearing an inertial sensor on the pelvis. Compared to Motion Analysis and video, the algorithm maintained a sensitivity of 0.90 and 0.76 and a specificity of 0.75 and 0.65, respectively. Second, we apply the turning algorithm to data collected in the home from 12 PD and 18 CT subjects. The algorithm successfully detects turn characteristics, and the results show that, compared to controls, PD subjects tend to take shorter turns with smaller turn angles and more steps. Furthermore, PD subjects show more variability in all turn metrics throughout the day and the week.
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The present findings are in line with other studies measuring remote physical activity in PD, which were traditionally performed over shorter timeframes (i.e., 1 day to 1 week).
“The present findings are also in line with other studies measuring remote physical activity in PD, which were traditionally performed over shorter timeframes (i.e., 1 day to 1 week).”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [48]
These reported significantly less motor activity in PD compared to controls (e.g., fewer steps and shorter bouts of physical activity), some of which were related to disease severity.
“These reported significantly less motor activity in PD compared to controls (e.g., fewer steps and shorter bouts of physical activity,48, 70) some of which were related to disease severity71”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [48]
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- Authors
- El-Gohary, M., Pearson, S., McNames, J., Mancini, M., Horak, F., Mellone, S., & Chiari, L.
- Affiliations
- APDM, Inc., Portland, OR 97201, USA
- APDM (United States) (US)
- Oregon Health & Science University (US)
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2013-12-27
- Volume
- 14
- Issue
- 1
- Pages
- 356-369
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC3926561
- OpenAlex
- W2067428313
- Cited by
- 258
- References
- 33
- Citations by year
- 2026: 17 · 2025: 20 · 2024: 16 · 2023: 21 · 2022: 26 · 2021: 27 · 2020: 25 · 2019: 28 · 2018: 23 · 2017: 29 · 2016: 16 · 2015: 5 · 2014: 5
- FWCI
- 22.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PDXScholar (Portland State University) (submittedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (R01AG006457)
- National Institute of Neurological Disorders and Stroke (R41NS076088)
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- turning, Parkinson's disease, mobility impairment, wearable inertial sensors, fall risk, freezing of gait, gait analysis, home monitoring, older adults, daily activities, turning angle, step count
- MeSH terms
- Aged, Algorithms, Female, Persons with Disabilities, Humans, Male, Middle Aged, Movement, Parkinson Disease, Biosensing Techniques
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