Abstract
Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson’s disease (PD) is missing. In this paper, we compare four prevalent gait segmentation methods in order to reveal their strengths and drawbacks in gait processing. We considered peak detection from event-based methods, two variations of dynamic time warping from template matching methods, and hierarchical hidden Markov models (hHMMs) from machine learning methods. To evaluate the methods, we included two supervised and instrumented gait tests that are widely used in the examination of Parkinsonian gait. In the first experiment, a sequence of strides from instructed straight walks was measured from 10 PD patients. In the second experiment, a more heterogeneous assessment paradigm was used from an additional 34 PD patients, including straight walks and turning strides as well as non-stride movements. The goal of the latter experiment was to evaluate the methods in challenging situations including turning strides and non-stride movements. Results showed no significant difference between the methods for the first scenario, in which all methods achieved an almost 100% accuracy in terms of F-score. Hence, we concluded that in the case of a predefined and homogeneous sequence of strides, all methods can be applied equally. However, in the second experiment the difference between methods became evident, with the hHMM obtaining a 96% F-score and significantly outperforming the other methods. The hHMM also proved promising in distinguishing between strides and non-stride movements, which is critical for clinical gait analysis. Our results indicate that both the instrumented test procedure and the required stride segmentation algorithm have to be selected adequately in order to support and complement classical clinical examination by sensor-based movement assessment.
Highlights
Parkinson’s disease (PD) is a neurodegenerative disorder with a prevalence of up to 2% in the elderly
PD diagnosis and monitoring is mainly based on standardized scoring methods such as the Unified Parkinson’s Disease Rating Scale (UPDRS) [3] and common techniques such as gait analysis, Timed Up-and-Go (TUG) [4], and the postural stability [5] test
The first experiment was performed in a leave-one-out cross validation scheme
Summary
Parkinson’s disease (PD) is a neurodegenerative disorder with a prevalence of up to 2% in the elderly. The most important impairments caused by PD are bradykinesia, rigidity, tremor, and postural. Sensors 2018, 18, 145 instability [1,2]. PD diagnosis and monitoring is mainly based on standardized scoring methods such as the Unified Parkinson’s Disease Rating Scale (UPDRS) [3] and common techniques such as gait analysis, Timed Up-and-Go (TUG) [4], and the postural stability [5] test. Low inter-rater reliability [6] for clinical assessment has been reported, necessitating complementary approaches in the clinical setting. There has been a growing interest in the monitoring of patients outside of the clinical environment e.g., in the case of long-term monitoring. The need to fulfill these goals has led to the development of a wide range of technical systems [7,8,9]
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