Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease

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...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 20; číslo 18; s. 5377
Hlavní autoři: Rehman, Rana Zia Ur, Klocke, Philipp, Hryniv, Sofia, Galna, Brook, Rochester, Lynn, Del Din, Silvia, Alcock, Lisa
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI 19.09.2020
MDPI AG
Témata:
Aged
Algorithms
Female
Gait Analysis
Gait Disorders, Neurologic - diagnosis
Humans
inertial measurement unit (IMU)
lower body
Male
Middle Aged
Parkinson Disease - diagnosis
signal-based characteristics
spatial-temporal characteristics
upper body
Walking
wearables
signal-based characteristics
inertial measurement unit (IMU)
lower body
upper body
PLS-DA
spatial-temporal characteristics
wearables
machine learning
validation
ISSN:1424-8220, 1424-8220
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Shrnutí: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s20185377