Objective: Multiple sclerosis (MS) is a chronic neurological condition of the central nervous system leading to various physical, mental and psychiatric complexities. Mobility limitations are amongst the most frequent and early markers of MS. We evaluated the effectiveness of a DeepMS2G (deep learning (DL) for MS differentiation using multi-stride dynamics in gait) framework, which is a DL-based methodology to classify multi-stride sequences of persons with MS (PwMS) from healthy controls (HC), in order to generalize over newer walking tasks and subjects. Methods: We collected single-task Walking and dualtask Walking-while-Talking gait data using an instrumented treadmill from a balanced collection of 20 HC and 20 PwMS. We utilized domain knowledge-based spatiotemporal and kinetic gait features along with two normalization schemes, namely standard size-based and multiple regression normalization strategies. To differentiate between multi-stride sequences of HC and PwMS, we compared 16 traditional machine learning and DL algorithms. Further, we studied the interpretability of our highest-performing models; and discussed the association between the lower extremity function of participants and our model predictions. Results: We observed that residual neural network (ResNet) based models with regression-based normalization were the top performers across both task and subject generalization classification designs. Considering regression-based normalization, a multi-scale ResNet attained a subject classification accuracy and F 1 -score of 1.0 when generalizing from single-task Walking to dual-task Walking-while-Talking; and a ResNet resulted in the top subject-wise accuracy and F 1 of 0.83 and 0.81 (resp.), when generalizing over Manuscript

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