Abstract
Gait tests as part of home monitoring study protocols for patients with movement disorders may provide valuable standardized anchor-points for real-world gait analysis using inertial measurement units (IMUs). However, analyzing unsupervised gait tests relies on reliable test annotations by the patients requiring a potentially error-prone interaction with the recording system. To overcome this limitation, this work presents a novel algorithmic pipeline for the automated detection of unsupervised standardized gait tests from continuous real-world IMU data. In a study with twelve Parkinson's disease patients, we recorded real-world gait data over two weeks using foot-worn IMUs. During continuous daily recordings, the participants performed series of three consecutive 4×10 -Meters-Walking-Tests ( 4×10 MWTs) at different walking speeds, besides their usual daily-living activities. The algorithm first detected these gait test series using a gait sequence detection algorithm, a peak enhancement pipeline, and subsequence Dynamic Time Warping and then decomposed them into single 4×10 MWTs based on the walking speed. In the evaluation with 419 available gait test series, the detection reached an F1-score of 88.9% and the decomposition an F1-score of 94.0%. A concurrent validity evaluation revealed very good agreement between spatio-temporal gait parameters derived from manually labelled and automatically detected 4×10 MWTs. Our algorithm allows to remove the burden of system interaction from the patients and reduces the time for manual data annotation for researchers. The study contributes to an improved automated processing of real-world IMU gait data and enables a simple integration of standardized tests into continuous long-term recordings. This will help to bridge the gap between supervised and unsupervised gait assessment.
Highlights
P ARKINSON’S disease (PD) is characterized by movement impairments in general and pathological gait in particular, including the cardinal symptoms tremor, rigidity, bradykinesia, and postural instability [1]
The parameteroptimized gait test series detection reached an F1-score of 88.9% (±3.0%), averaged over the test data sets in the 4-fold cross-validation (Table III)
Higher precision was observed for lower Θ and a more narrow turns threshold range, whereas the recall was increased for a higher cost threshold and a broader range
Summary
P ARKINSON’S disease (PD) is characterized by movement impairments in general and pathological gait in particular, including the cardinal symptoms tremor, rigidity, bradykinesia, and postural instability [1]. For a clinical assessment of these symptoms, rating scales are being applied, such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) [2]. Those scales lack objective quantitative measurements to evaluate motor symptoms and have a limited inter- and intra-rater reliability [3], [4]. Sensor-based gait analysis using wearable inertial measurement units (IMUs) has increasingly been used in clinical settings [5] and long-term monitoring in the real world [6] to provide complementary objective information on movement impairments. Several studies have demonstrated that long-term gait recordings have the potential to support monitoring of disease progression and symptoms, for example for the assessment of risk of falling [11]–[13]
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