2023
Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis
VanDyk T, Meyer B, DePetrillo P, Donahue N, O’Leary A, Fox S, Cheney N, Ceruolo M, Solomon A, McGinnis R. Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2023, 31: 2279-2286. PMID: 37115839, PMCID: PMC10408384, DOI: 10.1109/tnsre.2023.3271601.Peer-Reviewed Original ResearchConceptsPrediction of fall riskStanding transitionsDigital phenotypingFall riskNon-fallersSymptom monitoringPhysician assessmentMultiple sclerosisBiweekly assessmentsClinical metricsCharacterize symptomsHome monitoringSymptomsFatigueMotor instabilityAccelerometryFallersAssessmentPwMSPersonsApplications of wearablePhysiciansIsolation periodActivity classifierIntervention
2022
Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis
Meyer B, Tulipani L, Gurchiek R, Allen D, Solomon A, Cheney N, McGinnis R. Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis. PLOS Digital Health 2022, 1: e0000120. PMID: 36812538, PMCID: PMC9931255, DOI: 10.1371/journal.pdig.0000120.Peer-Reviewed Original ResearchWalking boutsFall riskGait parametersOpen-source datasetsNon-fallersFeature-based modelWalking bout durationClassification performanceWearable sensorsFall risk classificationDaily activity performancePatient-reported surveysBiannual clinical visitsFall risk estimationBout durationDeep learning modelsFall historyShort boutsClinic visitsInvestigate fall riskWalking dataRisk estimatesAssociated with morbiditySensor dataDeep learningEvaluation 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