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

Abstract

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.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

“…There is a lack of research investigating faller classification in patients from a diverse group of neurological conditions [28,70,71]. The classification results from our study were better compared to others.…”

“…Wearables have been used to assess gait in clinical and free-living conditions [23][24][25][26][27][28][29]. Gait characteristics obtained through signal-processing methods can be used to characterise fallers and non-fallers and these outcomes may be used to inform tailored intervention rehabilitation plans [30].…”

“…Fallers with neurological disorders (e.g., Parkinson's disease (PD)) showed higher variability in gait rhythmicity, higher asymmetry and a slower pace compared to non-fallers [31,32]. Gait characteristics measured with wearable devices can be used to train predictive models for accurate fall risk assessment [27,28]. However, methods for appropriately utilising this information for accurate fall risk assessment in people with neurological disorders are yet to be established.…”

“…All these studies involved healthy older adults or focussed on one patient group. Only one study [28] has considered a heterogeneous group of neurological disorders and reported maximum classification area under the curve (AUC) of 0.77 to distinguish fallers from non-fallers. For accurate fall prediction, classification accuracy needs to be improved.…”

Lower-Extremity Intra-Joint Coordination and Its Variability between Fallers and Non-Fallers during Gait

“…Biomechanical researchers have examined the spatiotemporal gait parameters and their variability among older adults as predictors of the risk of falling, as well as for differentiating between fallers and non-fallers [12,13]. Researchers suggested that the temporal variability and mean spatial parameters of gait have the highest efficacy in predicting the fall risk in clinical measurements [14,15], and that fallers walk with a considerably more irregular gait rhythm (shorter steps) than non-fallers [16]. Furthermore, increased variability of walking speed for defined and self-selected walking speeds and increased step width variability for fast walking speeds in fallers have been reported [12].…”