2022
Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis
Tulipani L, Meyer B, Allen D, Solomon A, McGinnis R. Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis. Gait & Posture 2022, 94: 19-25. PMID: 35220031, PMCID: PMC9086135, DOI: 10.1016/j.gaitpost.2022.02.016.Peer-Reviewed Original ResearchConceptsWearable sensorsFall statusFall riskUnsupervised conditionsChair stand test performanceClassification AUCUnsupervised monitoringChair stand testAccelerometer-derived metricsPredicting fall riskStandard Functional AssessmentSupervised performanceBalance confidenceFunctional mobilityWearableNon-fallersStand testBalance deficitsRoutine clinical assessmentSupervision visitsSensorThree-month periodPerformanceFunctional assessmentMultiple sclerosis
2021
Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis
Meyer B, Tulipani L, Gurchiek R, Allen D, Adamowicz L, Larie D, Solomon A, Cheney N, McGinnis R. Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis. IEEE Journal Of Biomedical And Health Informatics 2021, 25: 1824-1831. PMID: 32946403, PMCID: PMC8221405, DOI: 10.1109/jbhi.2020.3025049.Peer-Reviewed Original ResearchConceptsWearable sensorsPreventive interventionsBidirectional long short-termInexpensive wearable sensorsDeep neural networksWearable sensor dataFall prevention interventionsSpatiotemporal gait parametersFall risk assessmentLong-Short-TermMachine learning modelsGait biomechanicsGait parametersSensor dataFall riskNeural networkHealthcare providersStatistical featuresLearning modelsPatient reportsAccelerometer dataIdentified measuresMultiple sclerosisWearableGood performance