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Article

Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

1
Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 AV Groningen, The Netherlands
2
Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK
3
Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
4
Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(15), 4098; https://doi.org/10.3390/s20154098
Received: 17 June 2020 / Revised: 16 July 2020 / Accepted: 21 July 2020 / Published: 23 July 2020
Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (n = 384, age 49–80 s) were recruited from a neurology ward of a University hospital. They walked 20 m at a comfortable speed (single task: ST) and while performing a dual task with a motor component (DT1) and a dual task with a cognitive component (DT2). Twenty-seven spatial-temporal gait variables were measured with wearable sensors placed at the lower back and both ankles. Partial least square discriminant analysis (PLS-DA) was then applied to classify fallers and non-fallers. The PLS-DA classification model performed well for all three gait tasks (ST, DT1, and DT2) with an evaluation of classification performance Area under the receiver operating characteristic Curve (AUC) of 0.7, 0.6 and 0.7, respectively. Fallers differed from non-fallers in their specific gait patterns. Results from this study improve our understanding of how falls risk-related gait impairments in neurological patients could aid the design of tailored fall-prevention interventions. View Full-Text
Keywords: gait analysis; machine learning; inertial measurement units; neurological disorders; falls gait analysis; machine learning; inertial measurement units; neurological disorders; falls
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MDPI and ACS Style

Zhou, Y.; Zia Ur Rehman, R.; Hansen, C.; Maetzler, W.; Del Din, S.; Rochester, L.; Hortobágyi, T.; Lamoth, C.J.C. Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device. Sensors 2020, 20, 4098. https://doi.org/10.3390/s20154098

AMA Style

Zhou Y, Zia Ur Rehman R, Hansen C, Maetzler W, Del Din S, Rochester L, Hortobágyi T, Lamoth CJC. Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device. Sensors. 2020; 20(15):4098. https://doi.org/10.3390/s20154098

Chicago/Turabian Style

Zhou, Yuhan, Rana Zia Ur Rehman, Clint Hansen, Walter Maetzler, Silvia Del Din, Lynn Rochester, Tibor Hortobágyi, and Claudine J.C. Lamoth. 2020. "Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device" Sensors 20, no. 15: 4098. https://doi.org/10.3390/s20154098

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