Stable Sparse Classifiers predict cognitive impairment from gait patterns

Background Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. Purposes To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and older adults, as well as healthy and cognitively impaired elderly groups from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC. Methods A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification’s performance was assessed using the area under the receiver operating curves (AUC). The set of STGF features and tasks producing the most accurate classifications were identified. Results The comparison between the three groups revealed significant differences for all STGF in all tasks, while the global AUC of the classification using SSC was 0.87. The classification between the groups of elderly people revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86). Gait variability in step and stride time and the RMS value of vertical acceleration were the features with the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers. Conclusions The study corroborated that the changes in gait patterns can be used to discriminate between young and older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.