Distinguishing Pathologic Gait in Older Adults Using Instrumented Insoles and Deep Neural Networks

Abstract

Gait abnormalities are common in the older population owing to aging- and disease-related changes in physical and neurological functions. Differentiating the causes of gait abnormalities is challenging because various abnormal gaits share a similar pattern in older patients. Herein, we propose a deep neural network (DNN) model to classify disease-specific gait patterns in older adults using commercialized instrumented insoles. This study included 150 patients aged ≥ 65 years, divided into the following five groups (N = 30 in each group): healthy older individuals (HI), patients with Parkinson's disease (PD), patients with spastic hemiplegic gait due to stroke (SH), patients with normal-pressure hydrocephalus (NPH), and patients with knee osteoarthritis (OA). Participants performed the timed up and go test (TUGT) wearing the commercialized instrumented insole, GDCA-MD (Gilon, Republic of Korea). Seven data streams were collected from each insole using a 3-axis accelerometer and four pressure sensors and were analyzed. First, the statistical differences among groups in spatiotemporal features during TUGT, such as step count, step length, velocity, acceleration, regularity, and symmetricity, were examined. Second, a two-stage DNN model was developed that distinguishes HI from others in the first network and classifies the pathologic groups in the second network. The areas under the curve were 0.96, 0.88, 0.98, 0.96, and 0.97 for identifying HI, PD, OA, SH, and NPH, respectively. We demonstrated that the proposed DNN model can reliably classify gait abnormalities in an older population using simple instrumented insoles and a test.