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Cited by 258Balance, Gait, and Falls PreventionContext-Aware Activity Recognition SystemsGait Recognition and Analysis

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Continuous Monitoring of Turning in Patients with Movement Disability

Open access
El-Gohary, Mahmoud, Pearson, Sean, McNames, James, Mancini, Martina, Horak, Fay, Mellone, Sabato, Chiari, Lorenzo·2013·Sensors
DOI 10.3390/s140100356PMC PMC3926561
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Abstract

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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Figures

Figure 1. Inertial sensor, markers placement (back) and video camera attachment (front).
Figure 1. Inertial sensor, markers placement (back) and video camera attachment (front).CC BY · PubMed Central
Figure 2. An Opal with a Velcro strap in the docking station to recharge the device.
Figure 2. An Opal with a Velcro strap in the docking station to recharge the device.CC BY · PubMed Central
Figure 3. The blue trace is the X-Y position of the body center of mass from the optical markers. Overlaid in red and green are the segments detected as turns by the inertial algorithm and Motion Analysis, respectively.
Figure 3. The blue trace is the X-Y position of the body center of mass from the optical markers. Overlaid in red and green are the segments detected as turns by the inertial algorithm and Motion Analysis, respectively.CC BY · PubMed Central
Figure 4. The solid and dashed blue lines represent the yaw angle from Motion Analysis and the inertial algorithm, respectively. The dots and vertical dashed lines represent the onset (green) and end (red) of turns detected by Motion Analysis and the inertial algorithm, respectively.
Figure 4. The solid and dashed blue lines represent the yaw angle from Motion Analysis and the inertial algorithm, respectively. The dots and vertical dashed lines represent the onset (green) and end (red) of turns detected by Motion Analysis and the inertial algorithm, respectively.CC BY · PubMed Central
Figure 5. Rotational rate of the inertial sensors (Opals) attached to the lumbar (top), left foot (middle) and right foot (bottom). Blue, green and red traces are the x-, y- and z-axes of the gyroscope in degrees per second. Green areas represent periods in which the subject is not walking; gray represents periods of turning, and white areas represent periods of walking.
Figure 5. Rotational rate of the inertial sensors (Opals) attached to the lumbar (top), left foot (middle) and right foot (bottom). Blue, green and red traces are the x-, y- and z-axes of the gyroscope in degrees per second. Green areas represent periods in which the subject is not walking; gray represents periods of turning, and white areas represent periods of walking.CC BY · PubMed Central

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Metadata

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)
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2013-12-27
Volume
14
Issue
1
Pages
356-369
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s140100356
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
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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