Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait

Conclusion: We used advanced DL and dynamics across domain knowledge-based spatiotemporal and kinetic gait parameters to successfully classify MS gait across distinct walking trials and unseen participants. Significance: Our proposed DL algorithms might contribute to efforts to automate MS diagnoses.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research