Dynamic neural network approach to targeted balance assessment of individuals with and without neurological disease during non-steady-state locomotion

Nathaniel T Pickle,Staci M Shearin,Nicholas P Fey

doi:10.1186/s12984-019-0550-8

Abstract

BackgroundClinical balance assessments often rely on functional tasks as a proxy for balance (e.g., Timed Up and Go). In contrast, analyses of balance in research settings incorporate quantitative biomechanical measurements (e.g., whole-body angular momentum, H) using motion capture techniques. Fully instrumenting patients in the clinic is not feasible, and thus it is desirable to estimate biomechanical quantities related to balance from measurements taken from a subset of the body segments. Machine learning algorithms are well-suited for this type of low- to high-dimensional mapping. Thus, our goal was to develop and test an artificial neural network that to predict segment contributions to whole-body angular momentum from linear acceleration and angular velocity signals (i.e., those typically available to wearable inertial measurement units, IMUs) taken from a sparse set of body segments.MethodsOptical motion capture data were collected from five able-bodied individuals and five individuals with Parkinson's disease (PD) walking on a non-steady-state locomotor circuit comprising stairs, ramps and changes of direction. Motion data were used to calculate angular momentum (i.e., “gold standard” output data) and body-segment linear acceleration and angular velocity data from local reference frames at the wrists, ankles and neck (i.e., network input). A dynamic nonlinear autoregressive neural network was trained using the able-bodied data (pooled across subjects). The neural network was tested on data from individuals with PD with noise added to simulate real-world IMU data.ResultsCorrelation coefficients of the predicted segment contributions to whole-body angular momentum with the gold standard data were 0.989 for able-bodied individuals and 0.987 for individuals with PD. Mean RMS errors were between 2 and 7% peak signal magnitude for all body segments during completion of the locomotor circuits.ConclusionOur results suggest that estimating segment contributions to angular momentum from mechanical signals (linear acceleration, angular velocity) from a sparse set of body segments is a feasible method for assessing coordination of balance—even using a network trained on able-bodied data to assess individuals with neurological disease. These targeted estimates of segmental momenta could potentially be delivered to clinicians using a sparse sensor set (and likely in real-time) in order to enhance balance rehabilitation of people with PD.

Highlights

Clinical balance assessments often rely on functional tasks as a proxy for balance (e.g., Timed Up and Go)
In contrast with other conditions affecting balance through primarily mechanical changes, such as leg amputation, Parkinson’s disease (PD) results in significant neural changes that lead to impairment of motor learning [2] and cognitive tasks such as set shifting [3,4,5]
Each participant with PD was assessed to determine their Hoehn and Yahr stage, which is a widely used measure to grade the severity of PD as related to unilateral or bilateral motor involvement and gait/balance changes

Summary

Introduction

Clinical balance assessments often rely on functional tasks as a proxy for balance (e.g., Timed Up and Go). In contrast with other conditions affecting balance through primarily mechanical changes, such as leg amputation, PD results in significant neural changes that lead to impairment of motor learning [2] and cognitive tasks such as set shifting (unconsciously shifting attention between tasks) [3,4,5]. People with PD exhibit difficulty planning and executing complex motor tasks [7] and struggle to perform concurrent cognitive and motor tasks [8]. Their neurological condition impairs “self-regulation,” which is an ability that is significantly challenged during complex tasks. There is promising evidence that exercise, involving challenging balance tasks, can help to slow disease progression [9], aid neural plasticity [10], and improve functional ability [11, 12]

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