Abstract
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.
Highlights
Parkinson’s disease (PD) affects 145,000 people across the UK [1] and more than six million people worldwide, with prevalence expected to rise [2]
The groups were well matched for age (52 to 89 years), height, mass, body mass index (BMI) and global cognition (MMSE) (Table 1; p > 0.05)
We have demonstrated that wearable technology can be used to detect turns whilst walking accurately in older adults and people with PD, and turning characteristics collected from a range of upper and lower body locations can accurately distinguish between people with PD and age-matched controls
Summary
Parkinson’s disease (PD) affects 145,000 people across the UK [1] and more than six million people worldwide, with prevalence expected to rise [2]. Sensors 2020, 20, 5377; doi:10.3390/s20185377 www.mdpi.com/journal/sensors (excessive trunk flexion), reduced arm swing and resting tremor [3], which progress with disease duration [4]. Turning deficits in people with PD are evident for both the upper body (head and trunk) and lower body (legs) [6]. People with PD tend to turn more slowly with less angular rotation, meaning turning requires more steps [5,8]. People with PD are more likely to turn using multiple steps rather than a pivot turn on one leg [6]. Difficulty turning in people with PD is associated with clinical outcomes, such as falls, a fear of falling, disease severity, freezing of gait and cognitive impairment [9,10,11,12,13,14]
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