期刊
SENSORS
卷 20, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/s20185377
关键词
inertial measurement unit (IMU); wearables; upper body; lower body; spatial-temporal characteristics; signal-based characteristics; validation; machine learning; PLS-DA
资金
- Keep Control project, European Union's Horizon 2020 research and innovation ITN program under the Marie Sklodowska-Curie grant [721577]
- Parkinson's UK [J-0802, G-1301]
- National Institute for Health Research (NIHR) Newcastle Biomedical Research Center (BRC) based at Newcastle Upon Tyne Hospital NHS Foundation Trust
- Newcastle University (REC) [09/H0906/82]
- NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon Tyne Hospitals NHS Foundation Trust
Parkinson's disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC >= 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.
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