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

Abstract

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

A Pilot Study to Validate a Wearable Inertial Sensor for Gait Assessment in Older Adults with Falls

“…A frailty prevalence study [ 48 ] observed differences in the pattern and variability of all elderly walking. Frail individuals had a more significant number of shorter walking sessions and less variable in walking bout duration than non-frail older adults.…”

“…Frail individuals had a more significant number of shorter walking sessions and less variable in walking bout duration than non-frail older adults. All these features may be restricted to activities entailing short walking bouts (i.e., within the home environment), and they may be unable to sustain prolonged bouts of walking [ 48 ]. Furthermore, as Ternero-Quiñones et al [ 49 ] point out, frailty and the risk of falls are significant predictors of autonomy in basic daily life activities.…”

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…For the topic of falls, we identified 24 studies that met inclusion criteria. Of these studies, eight used a retrospective cohort design 40 41 42 43 44 45 46 47 ; seven used a prospective cohort design 48 49 50 51 52 53 54 ; six were secondary analyses of research data obtained from prospective, retrospective, and cross-sectional studies 55 56 57 58 59 60 ; one used mixed methods wherein data from a public dataset were used in conjunction with measurements collected from sensors 61 ; and one was a meta-analysis of prospective cohort and observational studies. 62 Ten of the studies used health records as a source of data but in two of these studies, 44 47 it was not clear whether the records were electronic when they were obtained.…”

“…Several of the studies, including two of the secondary analyses, incorporated data from mobility and gait sensors. 48 49 51 53 55 60 61 Registries and administrative datasets were used in eight studies, 40 41 42 43 45 46 50 56 while questionnaires or surveys were a source of data for four studies. 49 51 57 60 With the exception of one study that employed sensor data from 17-year-old persons, 55 all study participants were community dwelling, inpatient, and outpatient adults.…”

“…The conditions were postpolio syndrome, 51 neurology, neurosurgery, hematology, oncology, 52 and neurology. 53 Most studies were conducted in the United States, but studies were also completed in Italy, 49 England, 43 Japan, 44 51 Poland, 59 and South Korea. 52 The 11 studies included in the meta-analysis 62 were conducted in seven countries.…”