Early Identification of Gait Asymmetry Using a Dual-Channel Hybrid Deep Learning Model Based on a Wearable Sensor

Abstract

Background: Lack of an effective approach to distinguish the subtle differences between lower limb locomotion impedes early identification of gait asymmetry outdoors. This study aims to detect the significant discriminative characteristics associated with joint coupling changes between two lower limbs by using dual-channel deep learning and wearable sensors, helping to detect asymmetric gait early. Methods: The gait data of sensors attached on lower limb joints of twenty-four healthy subjects were acquired by using the Delsys TrignoTM system. Asymmetric gait was simulated by controlling ankle motion settings. The CNN–LSTM hybrid deep learning-based gait classification model with high-generalization, was developed to discriminate one normal limb gait and the other limb gait with four different settings, accurately measuring asymmetric gait. Results: Our developed model could reach a high accuracy of 98.61% to detect mild gait asymmetry, while obtaining an approximate accuracy of 50% to identify gait symmetry. The ankle contains more information about gait asymmetry than the hip and knee. Conclusions: Our technique could achieve excellent representation of learning capability to detect significantly discriminative gait features from dual-channels corresponding to the two lower limbs, even with subtle differences.