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Adaptive symptom reporting for mobile patient-reported disability assessment.

, , , , and . Wireless Health, page 172-179. IEEE, (2016)

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Relationship between gait variables and domains of neurologic dysfunction in multiple sclerosis using six-minute walk test., , and . EMBC, page 4959-4962. IEEE, (2016)Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis., , , and . BSN, page 1-6. IEEE, (2015)Longitudinal estimation of gait time series density in multiple sclerosis subjects using inertial data., , and . BSN, page 152. IEEE, (2016)Correlations between Inertial Body Sensor Measures and Clinical Measures in Multiple Sclerosis., , , and . BODYNETS, ICST, (2015)Demonstrating the real-world significance of the mid-swing to heel strike part of the gait cycle using spectral features., , , and . BSN, page 133-136. IEEE, (2017)Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis., , , , , , and . IEEE J. Biomed. Health Informatics, 22 (1): 40-46 (2018)Relationship between kernel density function estimates of gait time series and clinical data., , , and . BHI, page 329-332. IEEE, (2017)Deepmotion: a deep convolutional neural network on inertial body sensors for gait assessment in multiple sclerosis., , and . Wireless Health, page 164-171. IEEE, (2016)Adaptive symptom reporting for mobile patient-reported disability assessment., , , , and . Wireless Health, page 172-179. IEEE, (2016)Determining physiological significance of inertial gait features in multiple sclerosis., , , and . BSN, page 266-271. IEEE, (2016)